From d0aa2d7b1b6407d556fdf5551a9768af1404f2d7 Mon Sep 17 00:00:00 2001 From: punkduckable Date: Tue, 29 Oct 2024 13:00:35 +0000 Subject: [PATCH] deploy: fa16aa939537b093e3c89bff3d436a5a470228dc --- .doctrees/autoapi/lasdi/gp/index.doctree | Bin 37256 -> 13189 bytes .doctrees/autoapi/lasdi/inputs/index.doctree | Bin 29566 -> 18212 bytes .doctrees/autoapi/lasdi/param/index.doctree | Bin 98214 -> 37799 bytes .doctrees/autoapi/lasdi/timing/index.doctree | Bin 30089 -> 25051 bytes .doctrees/environment.pickle | Bin 1187015 -> 999402 bytes .doctrees/index.doctree | Bin 8119 -> 9071 bytes _sources/autoapi/lasdi/gp/index.rst.txt | 69 +----- _sources/autoapi/lasdi/inputs/index.rst.txt | 61 ++--- _sources/autoapi/lasdi/param/index.rst.txt | 222 ++---------------- _sources/autoapi/lasdi/timing/index.rst.txt | 23 -- _sources/index.rst.txt | 3 - autoapi/lasdi/gp/index.html | 77 ++----- autoapi/lasdi/inputs/index.html | 70 +++--- autoapi/lasdi/param/index.html | 225 +++++-------------- 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zE`aW#NfL4bkPI-Thf!-vX@+XX#LbJvH#4zj6$15aj*xo7Y?S2&WM(-7iP9d z!)<@L1t@l#@#G175I6M;Jc7FIBGg@bK=kHBac)MagRV1WOq^URXtX(4dJPj(#`(=G wa!;7mGL~d4EbZaUOUz9zsVqn>28U>EXU3Aw(hNyZ=vHOuWGvh)r4-5t0BnnKY5)KL diff --git a/_sources/autoapi/lasdi/gp/index.rst.txt b/_sources/autoapi/lasdi/gp/index.rst.txt index 7cedd0b..ebedee2 100644 --- a/_sources/autoapi/lasdi/gp/index.rst.txt +++ b/_sources/autoapi/lasdi/gp/index.rst.txt @@ -17,76 +17,27 @@ Functions Module Contents --------------- -.. py:function:: fit_gps(X: numpy.ndarray, Y: numpy.ndarray) -> list[sklearn.gaussian_process.GaussianProcessRegressor] - - Trains a GP for each column of Y. If Y has shape N x k, then we train k GP regressors. In this - case, we assume that X has shape N x M. Thus, the Input to the GP is in \mathbb{R}^M. For each - k, we train a GP where the i'th row of X is the input and the i,k component of Y is the - corresponding target. Thus, we return a list of k GP Regressor objects, the k'th one of which - makes predictions for the k'th coefficient in the latent dynamics. +.. py:function:: fit_gps(X, Y) + Trains each GP given the interpolation dataset. + X: (n_train, n_param) numpy 2d array + Y: (n_train, n_coef) numpy 2d array We assume each target coefficient is independent with each other. + gp_dictionnary is a dataset containing the trained GPs (as sklearn objects) - ----------------------------------------------------------------------------------------------- - :Parameters: * **X** (*A 2d numpy array of shape (n_train, input_dim), where n_train is the number of training*) - * **examples and input_dim is the number of components in each input (e.g., the number of** - * **parameters)** - * **Y** (*A 2d numpy array of shape (n_train, n_coef), where n_train is the number of training*) - * **examples and n_coef is the number of coefficients in the latent dynamics.** - - ----------------------------------------------------------------------------------------------- - :returns: * *A list of trained GP regressor objects. If Y has k columns, then the returned list has k* - * *elements. It's i'th element holds a trained GP regressor object whose training inputs are the* - * *columns of X and whose corresponding target values are the elements of the i'th column of Y.* - - -.. py:function:: eval_gp(gp_list: list[sklearn.gaussian_process.GaussianProcessRegressor], param_grid: numpy.ndarray) -> tuple +.. py:function:: eval_gp(gp_dictionnary, param_grid) Computes the GPs predictive mean and standard deviation for points of the parameter space grid - ----------------------------------------------------------------------------------------------- - :Parameters: * **gp_list** (*a list of trained GP regressor objects. The number of elements in this list should*) - * **match the number of columns in param_grid. The i'th element of this list is a GP regressor** - * **object that predicts the i'th coefficient.** - * **param_grid** (*A 2d numpy.ndarray object of shape (number of parameter combination, number of*) - * **parameters). The i,j element of this array specifies the value of the j'th parameter in the** - * **i'th combination of parameters. We use this as the testing set for the GP evaluation.** - - ----------------------------------------------------------------------------------------------- - :returns: * *A two element tuple. Both are 2d numpy arrays of shape (number of parameter combinations,* - * *number of coefficients). The two arrays hold the predicted means and std's for each parameter* - * *at each training example, respectively.* - * *Thus, the i,j element of the first return variable holds the predicted mean of the j'th* - * *coefficient in the latent dynamics at the i'th training example. Likewise, the i,j element of* - * *the second return variable holds the standard deviation in the predicted distribution for the* - * *j'th coefficient in the latent dynamics at the i'th combination of parameter values.* - - -.. py:function:: sample_coefs(gp_list: list[sklearn.gaussian_process.GaussianProcessRegressor], param: numpy.ndarray, n_samples: int) - - Generates sets of ODE (SINDy) coefficients sampled from the predictive distribution for those - coefficients at the specified parameter value (parma). Specifically, for the k'th SINDy - coefficient, we draw n_samples samples of the predictive distribution for the k'th coefficient - when param is the parameter. - - +.. py:function:: sample_coefs(gp_dictionnary, param, n_samples) - ----------------------------------------------------------------------------------------------- - :Parameters: * **gp_list** (*a list of trained GP regressor objects. The number of elements in this list should*) - * **match the number of columns in param_grid. The i'th element of this list is a GP regressor** - * **object that predicts the i'th coefficient.** - * **param** (*A combination of parameter values. i.e., a single test example. We evaluate each GP in*) - * **the gp_list at this parameter value (getting a prediction for each coefficient).** - * **n_samples** (*Number of samples of the predicted latent dynamics used to build ensemble of fom*) - * **predictions. N_s in the paper.** + Generates sample sets of ODEs for one given parameter. + coef_samples is a list of length n_samples, where each terms is a matrix of SINDy coefficients sampled from the GP predictive + distributions - ----------------------------------------------------------------------------------------------- - :returns: * *A 2d numpy ndarray object called coef_samples. It has shape (n_samples, n_coef), where n_coef* - * *is the number of coefficients (length of gp_list). The i,j element of this list is the i'th* - * *sample of the j'th SINDy coefficient.* diff --git a/_sources/autoapi/lasdi/inputs/index.rst.txt b/_sources/autoapi/lasdi/inputs/index.rst.txt index 9d6839f..e9a51ca 100644 --- a/_sources/autoapi/lasdi/inputs/index.rst.txt +++ b/_sources/autoapi/lasdi/inputs/index.rst.txt @@ -20,71 +20,46 @@ Classes lasdi.inputs.InputParser +Functions +--------- + +.. autoapisummary:: + + lasdi.inputs.getDictFromList + + Module Contents --------------- .. py:data:: verbose - :type: bool :value: False -.. py:class:: InputParser(dict: InputParser.__init__.dict, name: str = '') - - A InputParser objects acts as a wrapper around a dictionary of settings. Thus, each setting is - a key and the corresponding value is the setting's value. Because one setting may itself be - a dictionary (we often group settings; each group has a name but several constituent settings), - the underlying dictionary is structured as a sequence of nested dictionaries. This class allows - the user to select a specific setting from that structure by specifying (via a list of strings) - where in that nested structure the desired setting lives. - +.. py:class:: InputParser(dict, name='') .. py:attribute:: dict_ - :type: dict :value: None .. py:attribute:: name - :type: str :value: '' - .. py:method:: getInput(keys: list, fallback=None, datatype=None) - - A InputParser object acts as a wrapper around a dictionary of settings. That is, self.dict_ - is structured as a nested family of dictionaries. Each setting corresponds to a key in - self.dict_. The setting's value is the corresponding value in self.dict_. In many cases, - a particular setting may be nested within others. That is, a setting's value may itself be - another dictionary housing various sub-settings. This function allows us to fetch a - specific setting from this nested structure. - - Specifically, we specify a list of strings. keys[0] should be a key in self.dict_ - If so, we set val = self.dict_[keys[0]]. If there are more keys, then val should be a - dictionary and keys[1] should be a key in this dictionary. In this case, we replace val - with val[key[1]] and so on. This continues until we have exhausted all keys. There is one - important exception: - - If at some point in the process, there are more keys but val is not a dictionary, or if - there are more keys and val is a dictionary but the next key is not a key in that - dictionary, then we return the fallback value. If the fallback value does not exist, - returns an error. + .. py:method:: getInput(keys, fallback=None, datatype=None) + Find the value corresponding to the list of keys. + If the specified keys do not exist, use the fallback value. + If the fallback value does not exist, returns an error. + If the datatype is specified, enforce the output value has the right datatype. - ------------------------------------------------------------------------------------------- - :Parameters: * **keys** (*A list of keys we want to fetch from self.dict. keys[0] should be a key in self.dict_*) - * **If so, we set val = self.dict_[keys[0]]. If there are more keys, then val should be a** - * **dictionary and keys[1] should be a key in this dictionary. In this case, we replace val** - * **with val[key[1]] and so on. This continues until we have exhausted all keys.** - * **fallback** (*A sort of default value. If at some point, val is not a dictionary (and there are*) - * **more keys) or val is a dictionary but the next key is not a valid key in that dictionary,** - * **then we return the fallback value.** - * **datatype** (*If not None, then we require that the final val has this datatype. If the final*) - * **val does not have the desired datatype, we raise an exception.** - ------------------------------------------------------------------------------------------- - :rtype: The final val value as outlined by the process described above. +.. py:function:: getDictFromList(list_, inputDict) + get a dict with {key: val} from a list of dicts + NOTE: it returns only the first item in the list, + even if the list has more than one dict with {key: val}. diff --git a/_sources/autoapi/lasdi/param/index.rst.txt b/_sources/autoapi/lasdi/param/index.rst.txt index 5dfda82..dfc9691 100644 --- a/_sources/autoapi/lasdi/param/index.rst.txt +++ b/_sources/autoapi/lasdi/param/index.rst.txt @@ -32,167 +32,64 @@ Functions Module Contents --------------- -.. py:function:: get_1dspace_from_list(param_dict: dict) -> tuple[int, numpy.ndarray] - - This function generates the parameter range (set of possible parameter values) for a parameter - that uses the list type test space. That is, "test_space_type" should be a key for the - parameter dictionary and the corresponding value should be "list". The parameter dictionary - should also have a "list" key whose value is a list of the possible parameter values. - - We parse this list and turn it into a numpy ndarray. - - - ----------------------------------------------------------------------------------------------- - :Parameters: * **param_dict** (*A dictionary specifying one of the parameters. We should fetch this from the*) - * **configuration yaml file. It must have a "list" key whose corresponding value is a list of** - * **floats.** - - ----------------------------------------------------------------------------------------------- - :returns: * **Two arguments** (*Nx and paramRange. paramRange is a 1d numpy ndarray (whose ith value is the*) - * *i'th element of param_dict["list"]). Nx is the length of paramRange.* - - -.. py:function:: create_uniform_1dspace(param_dict: dict) -> tuple[int, numpy.ndarray] - - This function generates the parameter range (set of possible parameter values) for a parameter - that uses the uniform type test space. That is, "test_space_type" should be a key for the - parameter dictionary and the corresponding value should be "uniform". The parameter dictionary - should also have the following keys: - "min" - "max" - "sample_size" - "log_scale" - "min" and "max" specify the minimum and maximum value of the parameter, respectively. - "sample_size" specifies the number of parameter values we generate. Finally, log_scale, if - true, specifies if we should use a uniform or logarithmic spacing between samples of the - parameter. - - The values corresponding to "min" and "max" should be floats while the values corresponding to - "sample_size" and "log_scale" should be an int and a bool, respectively. - - - ----------------------------------------------------------------------------------------------- - :Parameters: * **param_dict** (*A dictionary specifying one of the parameters. We should fetch this from the*) - * **configuration yaml file. It must have a "min", "max", "sample_size", and "log_scale"** - * **keys (see above).** - - ----------------------------------------------------------------------------------------------- - :returns: * **Two arguments** (*Nx and paramRange. paramRange is a 1d numpy ndarray (whose ith value is the*) - * *i'th possible value of the parameter. Thus, paramRange[0] = param_dict["min"] and* - * *paramRange[-1] = param_dict["max"]). Nx is the length of paramRange or, equivalently* - * *param_dict["sample_size"].* +.. py:function:: get_1dspace_from_list(config) +.. py:function:: create_uniform_1dspace(config) .. py:data:: getParam1DSpace - :type: dict[str, callable] -.. py:class:: ParameterSpace(config: dict) +.. py:class:: ParameterSpace(config) .. py:attribute:: param_list - :type: list[dict] :value: [] .. py:attribute:: param_name - :type: list[str] :value: [] .. py:attribute:: n_param - :type: int :value: 0 .. py:attribute:: train_space - :type: numpy.ndarray :value: None .. py:attribute:: test_space - :type: numpy.ndarray :value: None .. py:attribute:: n_init - :type: int :value: 0 .. py:attribute:: test_grid_sizes - :type: list[int] :value: [] .. py:attribute:: test_meshgrid - :type: tuple[numpy.ndarray] :value: None - .. py:method:: n_train() -> int - - Returns the number of combinations of parameters in the training set. - - - - .. py:method:: n_test() -> int - - Returns the number of combinations of parameters in the testing set. - - - - .. py:method:: createInitialTrainSpace(param_list: list[dict]) -> numpy.ndarray - - Sets up a grid of parameter values to train at. Note that we only use the min and max value - of each parameter when setting up this grid. - - - ------------------------------------------------------------------------------------------- - :Parameters: * **param_list** (*A list of parameter dictionaries. Each entry should be a dictionary with the*) - * **following keys** -- - - - name - - min - - max - - ------------------------------------------------------------------------------------------- - :returns: * *A 2d array of shape ((2)^k, k), where k is the number of parameters (k == len(param_list)).* - * *The i'th column is the flattened i'th mesh_grid array we when we create a mesh grid using* - * *the min and max value of each parameter as the argument. See "createHyperMeshGrid" for* - * *details.* - * *Specifically, we return exactly what "createHyperGridSpace" returns. See the doc-string* - * *for that function for further details.* + .. py:method:: n_train() + .. py:method:: n_test() - .. py:method:: createTestGridSpace(param_list: list[dict]) -> tuple[list[int], tuple[numpy.ndarray], numpy.ndarray] - This function sets up a grid of parameter values to test at. + .. py:method:: createInitialTrainSpace(param_list) - ------------------------------------------------------------------------------------------- - :Parameters: * **param_list** (*A list of parameter dictionaries. Each dictionary should either use the*) - * **"uniform" or "list" format. See create_uniform_1dspace and get_1dspace_from_list,** - * **respectively.** - - ------------------------------------------------------------------------------------------- - :returns: * *A three element tuple.* - * *The first is a list whose i'th element specifies the number of distinct values of the i'th* - * *parameter we consider (this is the length of the i'th element of "paramRanges" below).* - * *The second is a a tuple of k numpy ndarrays (where k = len(param_list)), the i'th one of* - * *which is a k-dimensional array with shape (N0, ... , N{k - 1}), where Ni =* - * *param_list[i].size whose i(0), ... , i(k - 1) element specifies the value of the i'th* - * *parameter in the i(0), ... , i(k - 1)'th unique combination of parameter values.* - * *The third one is a 2d array of parameter values. It has shape (M, k), where* - * *M = \prod_{i = 0}^{k - 1} param_list[i].size.* - + .. py:method:: createTestGridSpace(param_list) .. py:method:: getParameter(param_vector) @@ -202,110 +99,35 @@ Module Contents - .. py:method:: createHyperMeshGrid(param_ranges: list[numpy.ndarray]) -> tuple[numpy.ndarray] - - This function generates arrays of parameter values. Specifically, if there are k - parameters (param_ranges has k elements), then we return k k-d arrays, the i'th one of - which is a k-dimensional array whose i(0), ... , i(k - 1) element specifies the value of - the i'th variable in the i(0), ... , i(k - 1)'th unique combination of parameter values. - - - ------------------------------------------------------------------------------------------- - :Parameters: * **param_ranges** (*list of numpy 1d arrays, each corresponding to 1d parameter grid space. The*) - * **i'th element of this list should be a 2-element numpy.ndarray object housing the max and** - * **min value for the i'th parameter. The list size should equal the number of parameters.** - - ------------------------------------------------------------------------------------------- - :returns: * *the "paramSpaces" tuple. This is a tuple of numpy ndarray objects, the i'th one of which* - * *gives the grid of parameter values for the i'th parameter. Specifically, if there are* - * *k parameters and if param_range[i].size = Ni, then the j'th return array has shape* - * *(N0, ... , N{k - 1}) and the i(0), ... , i(k - 1) element of this array houses the i(j)'th* - * *value of the j'th parameter.* - * *Thus, if there are k parameters, the returned tuple has k elements, each one of* - * *which is an array of shape (N0, ... , N{k - 1}).* - - - - .. py:method:: createHyperGridSpace(mesh_grids: tuple[numpy.ndarray]) -> numpy.ndarray - - Flattens the mesh_grid numpy.ndarray objects returned by createHyperMeshGrid and combines - them into a single 2d array of shape (grid size, number of parameters) (see below). - - - ------------------------------------------------------------------------------------------- - :Parameters: * **mesh_grids** (*tuple of numpy nd arrays, corresponding to each parameter. This should ALWAYS*) - * **be the output of the "CreateHyperMeshGrid" function. See the return section of that** - * **function's docstring for details.** - - ------------------------------------------------------------------------------------------- - :returns: * *The param_grid. This is a 2d numpy.ndarray object of shape (grid size, number of* - * *parameters). If each element of mesh_grids is a numpy.ndarray object of shape (N(1), ... ,* - * *N(k)) (k parameters), then (grid size) = N(1)*N(2)*...*N(k) and (number of parameters) = k.* - - - - .. py:method:: appendTrainSpace(param: numpy.ndarray) -> None - - Adds a new parameter to self's train space attribute. - - - ------------------------------------------------------------------------------------------- - :Parameters: * **param** (*A 1d numpy ndarray object. It should have shape (n_param) and should hold a*) - * **parameter value that we want to add to the training set.** - - ------------------------------------------------------------------------------------------- - :rtype: Nothing! - - - - .. py:method:: export() -> dict - - This function packages the testing/training examples into a dictionary, which it returns. - + .. py:method:: createHyperMeshGrid(param_ranges) - ------------------------------------------------------------------------------------------- - :Parameters: * **None!** - * **-------------------------------------------------------------------------------------------** + param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space. + The list size is equal to the number of parameters. - :returns: * *A dictionary with 4 keys. Below is a list of the keys with a short description of each* - * *corresponding value.* -- train_space: self.train_space, a 2d array of shape (n_train, n_param) whose i,j element - holds the value of the j'th parameter in the i'th training case. + Output: paramSpaces + - tuple of numpy nd arrays, corresponding to each parameter. + Dimension of the array equals to the number of parameters - test_space: self.test_space, a 2d array of shape (n_test, n_param) whose i,j element - holds the value of the j'th parameter in the i'th testing case. - test_grid_sizes: A list whose i'th element specifies how many distinct parameter values - we use for the i'th parameter. - test_meshgrid: a tuple of n_param numpy.ndarray array objects whose i'th element is a - n_param-dimensional array whose i(1), i(2), ... , i(n_param) element holds the value of - the i'th parameter in the i(1), ... , i(n_param) combination of parameter values in the - testing test. + .. py:method:: createHyperGridSpace(mesh_grids) - n_init: The number of combinations of training parameters in the training set. + mesh_grids: tuple of numpy nd arrays, corresponding to each parameter. + Dimension of the array equals to the number of parameters + Output: param_grid + - numpy 2d array of size (grid size x number of parameters). + grid size is the size of a numpy nd array. - .. py:method:: load(dict_: dict) -> None - This function builds a parameter space object from a dictionary. This dictionary should - be one that was returned by th export method, or a loaded copy of a dictionary that was - returned by the export method. + .. py:method:: appendTrainSpace(param) - ------------------------------------------------------------------------------------------- - :Parameters: * **dict_** (*This should be a dictionary with the following keys:*) -- - - train_space - - test_space - - test_grid_sizes - - test_meshgrid - - n_init - * **This dictionary should have been returned by the export method. We use the values in this** - * **dictionary to set up self.** + .. py:method:: export() - ------------------------------------------------------------------------------------------- - :rtype: Nothing! + .. py:method:: load(dict_) diff --git a/_sources/autoapi/lasdi/timing/index.rst.txt b/_sources/autoapi/lasdi/timing/index.rst.txt index 29846d6..2f919f9 100644 --- a/_sources/autoapi/lasdi/timing/index.rst.txt +++ b/_sources/autoapi/lasdi/timing/index.rst.txt @@ -107,30 +107,7 @@ Module Contents .. py:method:: export() - Export the list of jobs and their number of calls and total time - into a dictionary. - - Note: - All jobs must be ended before calling this method. - - Returns: - :obj:`dict` that contains "names", "calls", and "times" as keys - - .. py:method:: load(dict_) - Load the list of jobs and their number of calls and total time - from a dictionary. - - Args: - `dict_` (:obj:`dict`): Dictionary that contains the list of jobs and their calls and times. - - Note: - :obj:`dict_['names']`, :obj:`dict_['calls']` and :obj:`dict_['times']` must have the same size. - - Returns: - Does not return a value - - diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt index cb633a2..69cf38c 100644 --- a/_sources/index.rst.txt +++ b/_sources/index.rst.txt @@ -18,10 +18,7 @@ It also supports parametric interpolation of latent dynamics according to uncert References =================== -<<<<<<< HEAD * Bonneville, Christophe, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung et al. "A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling." arXiv preprint arXiv:2403.10748 (2024). -======= ->>>>>>> 9269e9cb2d85c993efecb52c726f2f1ff657d487 * Fries, William D., Xiaolong He, and Youngsoo Choi. "LaSDI: Parametric latent space dynamics identification." Computer Methods in Applied Mechanics and Engineering 399 (2022): 115436. * He, Xiaolong, Youngsoo Choi, William D. Fries, Jonathan L. Belof, and Jiun-Shyan Chen. "gLaSDI: Parametric physics-informed greedy latent space dynamics identification." Journal of Computational Physics 489 (2023): 112267. * Tran, April, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, and David M. Bortz. "Weak-form latent space dynamics identification." Computer Methods in Applied Mechanics and Engineering 427 (2024): 116998. diff --git a/autoapi/lasdi/gp/index.html b/autoapi/lasdi/gp/index.html index 7a4efa3..ff468ef 100644 --- a/autoapi/lasdi/gp/index.html +++ b/autoapi/lasdi/gp/index.html @@ -100,14 +100,14 @@

Functions

- - + + - + - - + +

fit_gps(...)

Trains a GP for each column of Y. If Y has shape N x k, then we train k GP regressors. In this

fit_gps(X, Y)

Trains each GP given the interpolation dataset.

eval_gp(→ tuple)

eval_gp(gp_dictionnary, param_grid)

Computes the GPs predictive mean and standard deviation for points of the parameter space grid

sample_coefs(gp_list, param, n_samples)

Generates sets of ODE (SINDy) coefficients sampled from the predictive distribution for those

sample_coefs(gp_dictionnary, param, n_samples)

Generates sample sets of ODEs for one given parameter.
@@ -116,71 +116,26 @@

Functions

-lasdi.gp.fit_gps(X: numpy.ndarray, Y: numpy.ndarray) → list[sklearn.gaussian_process.GaussianProcessRegressor]
-

Trains a GP for each column of Y. If Y has shape N x k, then we train k GP regressors. In this -case, we assume that X has shape N x M. Thus, the Input to the GP is in mathbb{R}^M. For each -k, we train a GP where the i’th row of X is the input and the i,k component of Y is the -corresponding target. Thus, we return a list of k GP Regressor objects, the k’th one of which -makes predictions for the k’th coefficient in the latent dynamics.

-

We assume each target coefficient is independent with each other.

-
-
    -

  • parameters)

    • -

  • Y (A 2d numpy array of shape (n_train, n_coef), where n_train is the number of training)

    • -

  • examples and n_coef is the number of coefficients in the latent dynamics.

    • -
-
-
-
    -

  • columns of X and whose corresponding target values are the elements of the i’th column of Y.

    • -
-
+lasdi.gp.fit_gps(X, Y) +

Trains each GP given the interpolation dataset. +X: (n_train, n_param) numpy 2d array +Y: (n_train, n_coef) numpy 2d array +We assume each target coefficient is independent with each other. +gp_dictionnary is a dataset containing the trained GPs (as sklearn objects)

-lasdi.gp.eval_gp(gp_list: list[sklearn.gaussian_process.GaussianProcessRegressor], param_grid: numpy.ndarray) → tuple
+lasdi.gp.eval_gp(gp_dictionnary, param_grid)

Computes the GPs predictive mean and standard deviation for points of the parameter space grid

-
-
    -

  • object that predicts the i’th coefficient.

    • -

  • param_grid (A 2d numpy.ndarray object of shape (number of parameter combination, number of)

    • -

  • parameters). The i,j element of this array specifies the value of the j’th parameter in the

    • -

  • i’th combination of parameters. We use this as the testing set for the GP evaluation.

    • -
-
-
-
    -

  • at each training example, respectively.

    • -

  • Thus, the i,j element of the first return variable holds the predicted mean of the j’th

    • -

  • coefficient in the latent dynamics at the i’th training example. Likewise, the i,j element of

    • -

  • the second return variable holds the standard deviation in the predicted distribution for the

    • -

  • j’th coefficient in the latent dynamics at the i’th combination of parameter values.

    • -
-
-lasdi.gp.sample_coefs(gp_list: list[sklearn.gaussian_process.GaussianProcessRegressor], param: numpy.ndarray, n_samples: int)
-

Generates sets of ODE (SINDy) coefficients sampled from the predictive distribution for those -coefficients at the specified parameter value (parma). Specifically, for the k’th SINDy -coefficient, we draw n_samples samples of the predictive distribution for the k’th coefficient -when param is the parameter.

-
-
    -

  • object that predicts the i’th coefficient.

    • -

  • param (A combination of parameter values. i.e., a single test example. We evaluate each GP in)

    • -

  • the gp_list at this parameter value (getting a prediction for each coefficient).

    • -

  • n_samples (Number of samples of the predicted latent dynamics used to build ensemble of fom)

    • -

  • predictions. N_s in the paper.

    • -
-
-
-
    -

  • sample of the j’th SINDy coefficient.

    • -
-
+lasdi.gp.sample_coefs(gp_dictionnary, param, n_samples) +

Generates sample sets of ODEs for one given parameter. +coef_samples is a list of length n_samples, where each terms is a matrix of SINDy coefficients sampled from the GP predictive +distributions

diff --git a/autoapi/lasdi/inputs/index.html b/autoapi/lasdi/inputs/index.html index 20ee2c3..81425e7 100644 --- a/autoapi/lasdi/inputs/index.html +++ b/autoapi/lasdi/inputs/index.html @@ -111,7 +111,17 @@

Classes

InputParser

-

A InputParser objects acts as a wrapper around a dictionary of settings. Thus, each setting is

+

+ + + + +
+

Functions

+ + + +

getDictFromList(list_, inputDict)

get a dict with {key: val} from a list of dicts
@@ -120,61 +130,39 @@

Classes

-lasdi.inputs.verbose: bool = False
+lasdi.inputs.verbose = False
-class lasdi.inputs.InputParser(dict: InputParser.__init__.dict, name: str = '')
-

A InputParser objects acts as a wrapper around a dictionary of settings. Thus, each setting is -a key and the corresponding value is the setting’s value. Because one setting may itself be -a dictionary (we often group settings; each group has a name but several constituent settings), -the underlying dictionary is structured as a sequence of nested dictionaries. This class allows -the user to select a specific setting from that structure by specifying (via a list of strings) -where in that nested structure the desired setting lives.

-
+class lasdi.inputs.InputParser(dict, name='') +
-dict_: dict = None
+dict_ = None
-name: str = ''
+name = ''
-getInput(keys: list, fallback=None, datatype=None)
-

A InputParser object acts as a wrapper around a dictionary of settings. That is, self.dict_ -is structured as a nested family of dictionaries. Each setting corresponds to a key in -self.dict_. The setting’s value is the corresponding value in self.dict_. In many cases, -a particular setting may be nested within others. That is, a setting’s value may itself be -another dictionary housing various sub-settings. This function allows us to fetch a -specific setting from this nested structure.

-

Specifically, we specify a list of strings. keys[0] should be a key in self.dict_ -If so, we set val = self.dict_[keys[0]]. If there are more keys, then val should be a -dictionary and keys[1] should be a key in this dictionary. In this case, we replace val -with val[key[1]] and so on. This continues until we have exhausted all keys. There is one -important exception:

-
-

If at some point in the process, there are more keys but val is not a dictionary, or if -there are more keys and val is a dictionary but the next key is not a key in that -dictionary, then we return the fallback value. If the fallback value does not exist, -returns an error.

-
-
-
    -

  • dictionary and keys[1] should be a key in this dictionary. In this case, we replace val

    • -

  • with val[key[1]] and so on. This continues until we have exhausted all keys.

    • -

  • fallback (A sort of default value. If at some point, val is not a dictionary (and there are)

    • -

  • more keys) or val is a dictionary but the next key is not a valid key in that dictionary,

    • -

  • then we return the fallback value.

    • -

  • datatype (If not None, then we require that the final val has this datatype. If the final)

    • -

  • val does not have the desired datatype, we raise an exception.

    • -
-
+getInput(keys, fallback=None, datatype=None) +

Find the value corresponding to the list of keys. +If the specified keys do not exist, use the fallback value. +If the fallback value does not exist, returns an error. +If the datatype is specified, enforce the output value has the right datatype.

+
+
+
+
+lasdi.inputs.getDictFromList(list_, inputDict)
+

get a dict with {key: val} from a list of dicts +NOTE: it returns only the first item in the list, +even if the list has more than one dict with {key: val}.

diff --git a/autoapi/lasdi/param/index.html b/autoapi/lasdi/param/index.html index 6485170..f88ae1e 100644 --- a/autoapi/lasdi/param/index.html +++ b/autoapi/lasdi/param/index.html @@ -120,11 +120,11 @@

Classes

- - + + - - + +

get_1dspace_from_list(→ tuple[int, numpy.ndarray])

This function generates the parameter range (set of possible parameter values) for a parameter

get_1dspace_from_list(config)

create_uniform_1dspace(→ tuple[int, numpy.ndarray])

This function generates the parameter range (set of possible parameter values) for a parameter

create_uniform_1dspace(config)
@@ -133,154 +133,81 @@

Functions

-lasdi.param.get_1dspace_from_list(param_dict: dict) → tuple[int, numpy.ndarray]
-

This function generates the parameter range (set of possible parameter values) for a parameter -that uses the list type test space. That is, “test_space_type” should be a key for the -parameter dictionary and the corresponding value should be “list”. The parameter dictionary -should also have a “list” key whose value is a list of the possible parameter values.

-

We parse this list and turn it into a numpy ndarray.

-
-
    -

  • floats.

    • -
-
-
+lasdi.param.get_1dspace_from_list(config) +
-lasdi.param.create_uniform_1dspace(param_dict: dict) → tuple[int, numpy.ndarray]
-

This function generates the parameter range (set of possible parameter values) for a parameter -that uses the uniform type test space. That is, “test_space_type” should be a key for the -parameter dictionary and the corresponding value should be “uniform”. The parameter dictionary -should also have the following keys:

-
-

“min” -“max” -“sample_size” -“log_scale”

-
-

“min” and “max” specify the minimum and maximum value of the parameter, respectively. -“sample_size” specifies the number of parameter values we generate. Finally, log_scale, if -true, specifies if we should use a uniform or logarithmic spacing between samples of the -parameter.

-

The values corresponding to “min” and “max” should be floats while the values corresponding to -“sample_size” and “log_scale” should be an int and a bool, respectively.

-
-
    -

  • keys (see above).

    • -
-
-
-
    -

  • paramRange[-1] = param_dict[“max”]). Nx is the length of paramRange or, equivalently

    • -

  • param_dict[“sample_size”].

    • -
-
-
+lasdi.param.create_uniform_1dspace(config) +
-lasdi.param.getParam1DSpace: dict[str, callable]
+lasdi.param.getParam1DSpace
-class lasdi.param.ParameterSpace(config: dict)
+class lasdi.param.ParameterSpace(config)
-param_list: list[dict] = []
+param_list = []
-param_name: list[str] = []
+param_name = []
-n_param: int = 0
+n_param = 0
-train_space: numpy.ndarray = None
+train_space = None
-test_space: numpy.ndarray = None
+test_space = None
-n_init: int = 0
+n_init = 0
-test_grid_sizes: list[int] = []
+test_grid_sizes = []
-test_meshgrid: tuple[numpy.ndarray] = None
+test_meshgrid = None
-n_train() → int
-

Returns the number of combinations of parameters in the training set.

-
+n_train() +
-n_test() → int
-

Returns the number of combinations of parameters in the testing set.

-
+n_test() +
-createInitialTrainSpace(param_list: list[dict]) → numpy.ndarray
-

Sets up a grid of parameter values to train at. Note that we only use the min and max value -of each parameter when setting up this grid.

-
-
    -

  • name

    • -

  • min

    • -

  • max

    • -
-
-
-
    -

  • the min and max value of each parameter as the argument. See “createHyperMeshGrid” for

    • -

  • details.

    • -

  • Specifically, we return exactly what “createHyperGridSpace” returns. See the doc-string

    • -

  • for that function for further details.

    • -
-
-
+createInitialTrainSpace(param_list) +
-createTestGridSpace(param_list: list[dict]) → tuple[list[int], tuple[numpy.ndarray], numpy.ndarray]
-

This function sets up a grid of parameter values to test at.

-
-
    -

  • respectively.

    • -
-
-
-
    -

  • parameter we consider (this is the length of the i’th element of “paramRanges” below).

    • -

  • The second is a a tuple of k numpy ndarrays (where k = len(param_list)), the i’th one of

    • -

  • which is a k-dimensional array with shape (N0, … , N{k - 1}), where Ni =

    • -

  • param_list[i].size whose i(0), … , i(k - 1) element specifies the value of the i’th

    • -

  • parameter in the i(0), … , i(k - 1)’th unique combination of parameter values.

    • -

  • The third one is a 2d array of parameter values. It has shape (M, k), where

    • -

  • M = prod_{i = 0}^{k - 1} param_list[i].size.

    • -
-
-
+createTestGridSpace(param_list) +
@@ -291,98 +218,46 @@

Module Contents
-createHyperMeshGrid(param_ranges: list[numpy.ndarray]) → tuple[numpy.ndarray]
-

This function generates arrays of parameter values. Specifically, if there are k -parameters (param_ranges has k elements), then we return k k-d arrays, the i’th one of -which is a k-dimensional array whose i(0), … , i(k - 1) element specifies the value of -the i’th variable in the i(0), … , i(k - 1)’th unique combination of parameter values.

-
-
    -

  • min value for the i’th parameter. The list size should equal the number of parameters.

    • -
-
-
-
    -

  • k parameters and if param_range[i].size = Ni, then the j’th return array has shape

    • -

  • (N0, … , N{k - 1}) and the i(0), … , i(k - 1) element of this array houses the i(j)’th

    • -

  • value of the j’th parameter.

    • -

  • Thus, if there are k parameters, the returned tuple has k elements, each one of

    • -

  • which is an array of shape (N0, … , N{k - 1}).

    • +createHyperMeshGrid(param_ranges)

+
+
param_ranges: list of numpy 1d arrays, each corresponding to 1d parameter grid space.

The list size is equal to the number of parameters.

+
+
Output: paramSpaces
    +

  • tuple of numpy nd arrays, corresponding to each parameter. +Dimension of the array equals to the number of parameters

- +
+
-createHyperGridSpace(mesh_grids: tuple[numpy.ndarray]) → numpy.ndarray
-

Flattens the mesh_grid numpy.ndarray objects returned by createHyperMeshGrid and combines -them into a single 2d array of shape (grid size, number of parameters) (see below).

-
-
    -

  • function’s docstring for details.

    • -
-
-
-
    -

  • N(k)) (k parameters), then (grid size) = N(1)*N(2)…*N(k) and (number of parameters) = k.*

    • +createHyperGridSpace(mesh_grids) +
    +
    mesh_grids: tuple of numpy nd arrays, corresponding to each parameter.

    Dimension of the array equals to the number of parameters

    +
    +
    Output: param_grid
      +

    • numpy 2d array of size (grid size x number of parameters).

    -
+

grid size is the size of a numpy nd array.

+
+
-appendTrainSpace(param: numpy.ndarray) → None
-

Adds a new parameter to self’s train space attribute.

-
+appendTrainSpace(param) +
-export() → dict
-

This function packages the testing/training examples into a dictionary, which it returns.

-
-
Returns:
-

    -

  • A dictionary with 4 keys. Below is a list of the keys with a short description of each

    • -

  • corresponding value. – train_space: self.train_space, a 2d array of shape (n_train, n_param) whose i,j element -holds the value of the j’th parameter in the i’th training case.

    -

    test_space: self.test_space, a 2d array of shape (n_test, n_param) whose i,j element -holds the value of the j’th parameter in the i’th testing case.

    -

    test_grid_sizes: A list whose i’th element specifies how many distinct parameter values -we use for the i’th parameter.

    -

    test_meshgrid: a tuple of n_param numpy.ndarray array objects whose i’th element is a -n_param-dimensional array whose i(1), i(2), … , i(n_param) element holds the value of -the i’th parameter in the i(1), … , i(n_param) combination of parameter values in the -testing test.

    -

    n_init: The number of combinations of training parameters in the training set.

    -
    • -
-

-
-
-
+export() +
-load(dict_: dict) → None
-

This function builds a parameter space object from a dictionary. This dictionary should -be one that was returned by th export method, or a loaded copy of a dictionary that was -returned by the export method.

-
-
-
    -

  • train_space

    • -

  • test_space

    • -

  • test_grid_sizes

    • -

  • test_meshgrid

    • -

  • n_init

    • -
-
-
    -

  • This dictionary should have been returned by the export method. We use the values in this

    • -

  • dictionary to set up self.

    • -
-
-
+load(dict_) +
diff --git a/autoapi/lasdi/timing/index.html b/autoapi/lasdi/timing/index.html index dce6b1d..2fbf4bb 100644 --- a/autoapi/lasdi/timing/index.html +++ b/autoapi/lasdi/timing/index.html @@ -200,30 +200,12 @@

Module Contents
export()
-

Export the list of jobs and their number of calls and total time -into a dictionary.

-
-
Note:

All jobs must be ended before calling this method.

-
-
Returns:

dict that contains “names”, “calls”, and “times” as keys

-
-
-
+
load(dict_)
-

Load the list of jobs and their number of calls and total time -from a dictionary.

-
-
Args:

dict_ (dict): Dictionary that contains the list of jobs and their calls and times.

-
-
Note:

dict_['names'], dict_['calls'] and dict_['times'] must have the same size.

-
-
Returns:

Does not return a value

-
-
-
+
diff --git a/genindex.html b/genindex.html index 565a6df..9e65c6b 100644 --- a/genindex.html +++ b/genindex.html @@ -286,10 +286,12 @@

G

  • get_new_sample_point() (lasdi.gplasdi.BayesianGLaSDI method)
    • -
  • getInput() (lasdi.inputs.InputParser method) +
  • getDictFromList() (in module lasdi.inputs)
      • +
    • getInput() (lasdi.inputs.InputParser method) +
    • getOperators() (lasdi.fd.Stencil method)
    • getParam1DSpace (in module lasdi.param) diff --git a/index.html b/index.html index 4bcb0f5..9380768 100644 --- a/index.html +++ b/index.html @@ -89,16 +89,15 @@

      LaSDI documentation

      References

      -

      <<<<<<< HEAD -* Bonneville, Christophe, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung et al. “A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling.” arXiv preprint arXiv:2403.10748 (2024). -======= ->>>>>>> 9269e9cb2d85c993efecb52c726f2f1ff657d487 -* Fries, William D., Xiaolong He, and Youngsoo Choi. “LaSDI: Parametric latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 399 (2022): 115436. -* He, Xiaolong, Youngsoo Choi, William D. Fries, Jonathan L. Belof, and Jiun-Shyan Chen. “gLaSDI: Parametric physics-informed greedy latent space dynamics identification.” Journal of Computational Physics 489 (2023): 112267. -* Tran, April, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, and David M. Bortz. “Weak-form latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 427 (2024): 116998. -* Park, Jun Sur Richard, Siu Wun Cheung, Youngsoo Choi, and Yeonjong Shin. “tLaSDI: Thermodynamics-informed latent space dynamics identification.” arXiv preprint arXiv:2403.05848 (2024). -* Bonneville, Christophe, Youngsoo Choi, Debojyoti Ghosh, and Jonathan L. Belof. “Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder.” Computer Methods in Applied Mechanics and Engineering 418 (2024): 116535. -* He, Xiaolong, April Tran, David M. Bortz, and Youngsoo Choi. “Physics-informed active learning with simultaneous weak-form latent space dynamics identification.” arXiv preprint arXiv:2407.00337 (2024).

      +
        +

      • Bonneville, Christophe, Xiaolong He, April Tran, Jun Sur Park, William Fries, Daniel A. Messenger, Siu Wun Cheung et al. “A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling.” arXiv preprint arXiv:2403.10748 (2024).

        • +

      • Fries, William D., Xiaolong He, and Youngsoo Choi. “LaSDI: Parametric latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 399 (2022): 115436.

        • +

      • He, Xiaolong, Youngsoo Choi, William D. Fries, Jonathan L. Belof, and Jiun-Shyan Chen. “gLaSDI: Parametric physics-informed greedy latent space dynamics identification.” Journal of Computational Physics 489 (2023): 112267.

        • +

      • Tran, April, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, and David M. Bortz. “Weak-form latent space dynamics identification.” Computer Methods in Applied Mechanics and Engineering 427 (2024): 116998.

        • +

      • Park, Jun Sur Richard, Siu Wun Cheung, Youngsoo Choi, and Yeonjong Shin. “tLaSDI: Thermodynamics-informed latent space dynamics identification.” arXiv preprint arXiv:2403.05848 (2024).

        • +

      • Bonneville, Christophe, Youngsoo Choi, Debojyoti Ghosh, and Jonathan L. Belof. “Gplasdi: Gaussian process-based interpretable latent space dynamics identification through deep autoencoder.” Computer Methods in Applied Mechanics and Engineering 418 (2024): 116535.

        • +

      • He, Xiaolong, April Tran, David M. Bortz, and Youngsoo Choi. “Physics-informed active learning with simultaneous weak-form latent space dynamics identification.” arXiv preprint arXiv:2407.00337 (2024).

        • +
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"lasdi.fd.SBP48.leftBdrDepth"]], "leftbdrdepth (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrDepth"]], "leftbdrnorm (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrNorm"]], "leftbdrnorm (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrNorm"]], "leftbdrstencils (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrStencils"]], "leftbdrstencils (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrStencils"]], "leftbdrwidth (lasdi.fd.sbp12 attribute)": [[2, "lasdi.fd.SBP12.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.sbp24 attribute)": [[2, "lasdi.fd.SBP24.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.sbp36 attribute)": [[2, "lasdi.fd.SBP36.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.sbp48 attribute)": [[2, "lasdi.fd.SBP48.leftBdrWidth"]], "leftbdrwidth (lasdi.fd.stencil attribute)": [[2, "lasdi.fd.Stencil.leftBdrWidth"]], "eval_gp() (in module lasdi.gp)": [[3, "lasdi.gp.eval_gp"]], "fit_gps() (in module lasdi.gp)": [[3, "lasdi.gp.fit_gps"]], "lasdi.gp": [[3, "module-lasdi.gp"]], "sample_coefs() (in module lasdi.gp)": [[3, "lasdi.gp.sample_coefs"]], "bayesianglasdi (class in lasdi.gplasdi)": [[4, "lasdi.gplasdi.BayesianGLaSDI"]], "mse (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.MSE"]], "x_test (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.X_test"]], "x_train (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.X_train"]], "autoencoder (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.autoencoder"]], "average_rom() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.average_rom"]], "best_coefs (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.best_coefs"]], "best_loss (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.best_loss"]], "coef_weight (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.coef_weight"]], "export() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.export"]], "get_fom_max_std() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.get_fom_max_std"]], "get_new_sample_point() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.get_new_sample_point"]], "lasdi.gplasdi": [[4, "module-lasdi.gplasdi"]], "latent_dynamics (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.latent_dynamics"]], "ld_weight (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.ld_weight"]], "load() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.load"]], "lr (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.lr"]], "max_greedy_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.max_greedy_iter"]], "max_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.max_iter"]], "n_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.n_iter"]], "n_samples (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.n_samples"]], "optimizer (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.optimizer"]], "optimizer_to() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.optimizer_to"]], "param_space (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.param_space"]], "path_checkpoint (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.path_checkpoint"]], "path_results (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.path_results"]], "physics (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.physics"]], "restart_iter (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.restart_iter"]], "sample_roms() (in module lasdi.gplasdi)": [[4, "lasdi.gplasdi.sample_roms"]], "timer (lasdi.gplasdi.bayesianglasdi attribute)": [[4, "lasdi.gplasdi.BayesianGLaSDI.timer"]], "train() (lasdi.gplasdi.bayesianglasdi method)": [[4, "lasdi.gplasdi.BayesianGLaSDI.train"]], "lasdi": [[5, "module-lasdi"]], "inputparser (class in lasdi.inputs)": [[6, "lasdi.inputs.InputParser"]], "dict_ (lasdi.inputs.inputparser attribute)": [[6, "lasdi.inputs.InputParser.dict_"]], "getinput() (lasdi.inputs.inputparser method)": [[6, "lasdi.inputs.InputParser.getInput"]], "lasdi.inputs": [[6, "module-lasdi.inputs"]], "name (lasdi.inputs.inputparser attribute)": [[6, "lasdi.inputs.InputParser.name"]], "verbose (in module lasdi.inputs)": [[6, "lasdi.inputs.verbose"]], "latentdynamics (class in lasdi.latent_dynamics)": [[7, "lasdi.latent_dynamics.LatentDynamics"]], "calibrate() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.calibrate"]], "coefs (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.coefs"]], "dim (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.dim"]], "export() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.export"]], "lasdi.latent_dynamics": [[7, "module-lasdi.latent_dynamics"]], "load() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.load"]], "ncoefs (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.ncoefs"]], "nt (lasdi.latent_dynamics.latentdynamics attribute)": [[7, "lasdi.latent_dynamics.LatentDynamics.nt"]], "sample() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.sample"]], "simulate() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.simulate"]], "mse (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.MSE"]], "sindy (class in lasdi.latent_dynamics.sindy)": [[8, "lasdi.latent_dynamics.sindy.SINDy"]], "calibrate() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.calibrate"]], "coef_norm_order (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.coef_norm_order"]], "compute_time_derivative() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.compute_time_derivative"]], "export() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.export"]], "fd (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd"]], "fd_oper (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd_oper"]], "fd_type (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd_type"]], "lasdi.latent_dynamics.sindy": [[8, "module-lasdi.latent_dynamics.sindy"]], "ncoefs (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.ncoefs"]], "simulate() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.simulate"]], "autoencoder (class in lasdi.latent_space)": [[9, "lasdi.latent_space.Autoencoder"]], "multilayerperceptron (class in lasdi.latent_space)": [[9, "lasdi.latent_space.MultiLayerPerceptron"]], "act_dict (in module lasdi.latent_space)": [[9, "lasdi.latent_space.act_dict"]], "act_type (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.act_type"]], "decoder (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.decoder"]], "encoder (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.encoder"]], "export() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.export"]], "forward() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.forward"]], "forward() (lasdi.latent_space.multilayerperceptron method)": [[9, "lasdi.latent_space.MultiLayerPerceptron.forward"]], "init_weight() (lasdi.latent_space.multilayerperceptron method)": [[9, "lasdi.latent_space.MultiLayerPerceptron.init_weight"]], "initial_condition_latent() (in module lasdi.latent_space)": [[9, "lasdi.latent_space.initial_condition_latent"]], "lasdi.latent_space": [[9, "module-lasdi.latent_space"]], "layer_sizes (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.layer_sizes"]], "layers (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.layers"]], "load() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.load"]], "n_layers (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.n_layers"]], "n_z (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.n_z"]], "qgrid_size (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.qgrid_size"]], "reshape_index (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.reshape_index"]], "reshape_shape (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.reshape_shape"]], "space_dim (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.space_dim"]], "parameterspace (class in lasdi.param)": [[10, "lasdi.param.ParameterSpace"]], "appendtrainspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.appendTrainSpace"]], "createhypergridspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createHyperGridSpace"]], "createhypermeshgrid() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createHyperMeshGrid"]], "createinitialtrainspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createInitialTrainSpace"]], "createtestgridspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createTestGridSpace"]], "create_uniform_1dspace() (in module lasdi.param)": [[10, "lasdi.param.create_uniform_1dspace"]], "export() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.export"]], "getparam1dspace (in module lasdi.param)": [[10, "lasdi.param.getParam1DSpace"]], "getparameter() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.getParameter"]], "get_1dspace_from_list() (in module lasdi.param)": [[10, "lasdi.param.get_1dspace_from_list"]], "lasdi.param": [[10, "module-lasdi.param"]], "load() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.load"]], "n_init (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.n_init"]], "n_param (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.n_param"]], "n_test() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.n_test"]], "n_train() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.n_train"]], "param_list (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.param_list"]], "param_name (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.param_name"]], "test_grid_sizes (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_grid_sizes"]], "test_meshgrid (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_meshgrid"]], "test_space (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_space"]], "train_space (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.train_space"]], "burgers1d (class in lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.Burgers1D"]], "a_idx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.a_idx"]], "convergence_threshold (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.convergence_threshold"]], "dim (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.dim"]], "dt (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.dt"]], "dx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.dx"]], "export() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.export"]], "grid_size (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.grid_size"]], "initial_condition() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.initial_condition"]], "jacobian() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.jacobian"]], "lasdi.physics.burgers1d": [[11, "module-lasdi.physics.burgers1d"]], "main() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.main"]], "maxk (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.maxk"]], "nt (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.nt"]], "offline (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.offline"]], "qdim (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.qdim"]], "qgrid_size (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.qgrid_size"]], "residual() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.residual"]], "residual_burgers() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.residual_burgers"]], "solve() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.solve"]], "solver() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.solver"]], "t_grid (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.t_grid"]], "tmax (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.tmax"]], "w_idx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.w_idx"]], "x_grid (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.x_grid"]], "xmax (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.xmax"]], "xmin (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.xmin"]], "offlinefom (class in lasdi.physics)": [[12, "lasdi.physics.OfflineFOM"]], "physics (class in lasdi.physics)": [[12, "lasdi.physics.Physics"]], "dim (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.dim"]], "dim (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.dim"]], "dt (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.dt"]], "dt (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.dt"]], "export() (lasdi.physics.offlinefom method)": [[12, "lasdi.physics.OfflineFOM.export"]], "export() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.export"]], "generate_solutions() (lasdi.physics.offlinefom method)": [[12, "lasdi.physics.OfflineFOM.generate_solutions"]], "generate_solutions() (lasdi.physics.physics method)": [[12, 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(lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.sample"]], "simulate() (lasdi.latent_dynamics.latentdynamics method)": [[7, "lasdi.latent_dynamics.LatentDynamics.simulate"]], "mse (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.MSE"]], "sindy (class in lasdi.latent_dynamics.sindy)": [[8, "lasdi.latent_dynamics.sindy.SINDy"]], "calibrate() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.calibrate"]], "coef_norm_order (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.coef_norm_order"]], "compute_time_derivative() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.compute_time_derivative"]], "export() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.export"]], "fd (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd"]], "fd_oper (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd_oper"]], "fd_type (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.fd_type"]], "lasdi.latent_dynamics.sindy": [[8, "module-lasdi.latent_dynamics.sindy"]], "ncoefs (lasdi.latent_dynamics.sindy.sindy attribute)": [[8, "lasdi.latent_dynamics.sindy.SINDy.ncoefs"]], "simulate() (lasdi.latent_dynamics.sindy.sindy method)": [[8, "lasdi.latent_dynamics.sindy.SINDy.simulate"]], "autoencoder (class in lasdi.latent_space)": [[9, "lasdi.latent_space.Autoencoder"]], "multilayerperceptron (class in lasdi.latent_space)": [[9, "lasdi.latent_space.MultiLayerPerceptron"]], "act_dict (in module lasdi.latent_space)": [[9, "lasdi.latent_space.act_dict"]], "act_type (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.act_type"]], "decoder (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.decoder"]], "encoder (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.encoder"]], "export() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.export"]], "forward() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.forward"]], "forward() (lasdi.latent_space.multilayerperceptron method)": [[9, "lasdi.latent_space.MultiLayerPerceptron.forward"]], "init_weight() (lasdi.latent_space.multilayerperceptron method)": [[9, "lasdi.latent_space.MultiLayerPerceptron.init_weight"]], "initial_condition_latent() (in module lasdi.latent_space)": [[9, "lasdi.latent_space.initial_condition_latent"]], "lasdi.latent_space": [[9, "module-lasdi.latent_space"]], "layer_sizes (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.layer_sizes"]], "layers (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.layers"]], "load() (lasdi.latent_space.autoencoder method)": [[9, "lasdi.latent_space.Autoencoder.load"]], "n_layers (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.n_layers"]], "n_z (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.n_z"]], "qgrid_size (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.qgrid_size"]], "reshape_index (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.reshape_index"]], "reshape_shape (lasdi.latent_space.multilayerperceptron attribute)": [[9, "lasdi.latent_space.MultiLayerPerceptron.reshape_shape"]], "space_dim (lasdi.latent_space.autoencoder attribute)": [[9, "lasdi.latent_space.Autoencoder.space_dim"]], "parameterspace (class in lasdi.param)": [[10, "lasdi.param.ParameterSpace"]], "appendtrainspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.appendTrainSpace"]], "createhypergridspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createHyperGridSpace"]], "createhypermeshgrid() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createHyperMeshGrid"]], "createinitialtrainspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createInitialTrainSpace"]], "createtestgridspace() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.createTestGridSpace"]], "create_uniform_1dspace() (in module lasdi.param)": [[10, "lasdi.param.create_uniform_1dspace"]], "export() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.export"]], "getparam1dspace (in module lasdi.param)": [[10, "lasdi.param.getParam1DSpace"]], "getparameter() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.getParameter"]], "get_1dspace_from_list() (in module lasdi.param)": [[10, "lasdi.param.get_1dspace_from_list"]], "lasdi.param": [[10, "module-lasdi.param"]], "load() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.load"]], "n_init (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.n_init"]], "n_param (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.n_param"]], "n_test() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.n_test"]], "n_train() (lasdi.param.parameterspace method)": [[10, "lasdi.param.ParameterSpace.n_train"]], "param_list (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.param_list"]], "param_name (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.param_name"]], "test_grid_sizes (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_grid_sizes"]], "test_meshgrid (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_meshgrid"]], "test_space (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.test_space"]], "train_space (lasdi.param.parameterspace attribute)": [[10, "lasdi.param.ParameterSpace.train_space"]], "burgers1d (class in lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.Burgers1D"]], "a_idx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.a_idx"]], "convergence_threshold (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.convergence_threshold"]], "dim (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.dim"]], "dt (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.dt"]], "dx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.dx"]], "export() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.export"]], "grid_size (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.grid_size"]], "initial_condition() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.initial_condition"]], "jacobian() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.jacobian"]], "lasdi.physics.burgers1d": [[11, "module-lasdi.physics.burgers1d"]], "main() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.main"]], "maxk (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.maxk"]], "nt (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.nt"]], "offline (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.offline"]], "qdim (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.qdim"]], "qgrid_size (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.qgrid_size"]], "residual() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.residual"]], "residual_burgers() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.residual_burgers"]], "solve() (lasdi.physics.burgers1d.burgers1d method)": [[11, "lasdi.physics.burgers1d.Burgers1D.solve"]], "solver() (in module lasdi.physics.burgers1d)": [[11, "lasdi.physics.burgers1d.solver"]], "t_grid (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.t_grid"]], "tmax (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.tmax"]], "w_idx (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.w_idx"]], "x_grid (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.x_grid"]], "xmax (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.xmax"]], "xmin (lasdi.physics.burgers1d.burgers1d attribute)": [[11, "lasdi.physics.burgers1d.Burgers1D.xmin"]], "offlinefom (class in lasdi.physics)": [[12, "lasdi.physics.OfflineFOM"]], "physics (class in lasdi.physics)": [[12, "lasdi.physics.Physics"]], "dim (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.dim"]], "dim (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.dim"]], "dt (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.dt"]], "dt (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.dt"]], "export() (lasdi.physics.offlinefom method)": [[12, "lasdi.physics.OfflineFOM.export"]], "export() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.export"]], "generate_solutions() (lasdi.physics.offlinefom method)": [[12, "lasdi.physics.OfflineFOM.generate_solutions"]], "generate_solutions() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.generate_solutions"]], "grid_size (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.grid_size"]], "grid_size (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.grid_size"]], "initial_condition() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.initial_condition"]], "lasdi.physics": [[12, "module-lasdi.physics"]], "nt (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.nt"]], "nt (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.nt"]], "offline (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.offline"]], "offline (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.offline"]], "param_name (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.param_name"]], "qdim (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.qdim"]], "qdim (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.qdim"]], "qgrid_size (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.qgrid_size"]], "qgrid_size (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.qgrid_size"]], "residual() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.residual"]], "solve() (lasdi.physics.physics method)": [[12, "lasdi.physics.Physics.solve"]], "t_grid (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.t_grid"]], "t_grid (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.t_grid"]], "x_grid (lasdi.physics.offlinefom attribute)": [[12, "lasdi.physics.OfflineFOM.x_grid"]], "x_grid (lasdi.physics.physics attribute)": [[12, "lasdi.physics.Physics.x_grid"]], "compute_errors() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.compute_errors"]], "heatmap2d() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.heatmap2d"]], "lasdi.postprocess": [[13, "module-lasdi.postprocess"]], "plot_gp2d() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.plot_gp2d"]], "plot_prediction() (in module lasdi.postprocess)": [[13, "lasdi.postprocess.plot_prediction"]], "timer (class in lasdi.timing)": [[14, "lasdi.timing.Timer"]], "calls (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.calls"]], "end() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.end"]], "export() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.export"]], "lasdi.timing": [[14, "module-lasdi.timing"]], "load() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.load"]], "names (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.names"]], "print() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.print"]], "start() (lasdi.timing.timer method)": [[14, "lasdi.timing.Timer.start"]], "starts (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.starts"]], "times (lasdi.timing.timer attribute)": [[14, "lasdi.timing.Timer.times"]], "collect_samples() (in module lasdi.workflow)": [[15, "lasdi.workflow.collect_samples"]], "initialize_latent_space() (in module lasdi.workflow)": [[15, "lasdi.workflow.initialize_latent_space"]], "initialize_physics() (in module lasdi.workflow)": [[15, "lasdi.workflow.initialize_physics"]], "initialize_trainer() (in module lasdi.workflow)": [[15, "lasdi.workflow.initialize_trainer"]], "lasdi.workflow": [[15, "module-lasdi.workflow"]], "latent_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.latent_dict"]], "ld_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.ld_dict"]], "main() (in module lasdi.workflow)": [[15, "lasdi.workflow.main"]], "parser (in module lasdi.workflow)": [[15, "lasdi.workflow.parser"]], "physics_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.physics_dict"]], "pick_samples() (in module lasdi.workflow)": [[15, "lasdi.workflow.pick_samples"]], "run_samples() (in module lasdi.workflow)": [[15, "lasdi.workflow.run_samples"]], "step() (in module lasdi.workflow)": [[15, "lasdi.workflow.step"]], "trainer_dict (in module lasdi.workflow)": [[15, "lasdi.workflow.trainer_dict"]]}}) \ No newline at end of file