Merge branch 'master' into docs_update

.github/workflows/CI.yml

@@ -3,9 +3,13 @@ on:
   pull_request:
     branches:
       - master
+    paths-ignore:
+      - 'docs/**'
   push:
     branches:
       - master
+    paths-ignore:
+      - 'docs/**'
 jobs:
   test:
     runs-on: ubuntu-latest

Project.toml

@@ -63,7 +63,7 @@ Lux = "0.4, 0.5"
 MCMCChains = "6"
 ModelingToolkit = "8"
 MonteCarloMeasurements = "1"
-Optim = "1.0"
+Optim = "=1.7.6"
 Optimisers = "0.2, 0.3"
 Optimization = "3"
 QuasiMonteCarlo = "0.2.1"

README.md

@@ -37,7 +37,7 @@ the documentation, which contains the unreleased features.
   - Integrated logging suite for handling connections to TensorBoard
   - Handling of (partial) integro-differential equations and various stochastic equations
   - Specialized forms for solving `ODEProblem`s with neural networks
-  - Compatability with [Flux.jl](https://docs.sciml.ai/Flux.jl/stable/) and [Lux.jl](https://docs.sciml.ai/Lux/stable/)
+  - Compatability with [Flux.jl](https://fluxml.ai/) and [Lux.jl](https://lux.csail.mit.edu/)
     for all of the GPU-powered machine learning layers available from those libraries.
   - Compatability with [NeuralOperators.jl](https://docs.sciml.ai/NeuralOperators/stable/) for
     mixing DeepONets and other neural operators (Fourier Neural Operators, Graph Neural Operators,

docs/Project.toml

@@ -20,7 +20,7 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 
 [compat]
 DiffEqBase = "6.106"
-Documenter = "0.27"
+Documenter = "1"
 DomainSets = "0.6"
 Flux = "0.13, 0.14"
 Integrals = "3.3"

docs/make.jl

@@ -10,19 +10,10 @@ include("pages.jl")
 
 makedocs(sitename = "NeuralPDE.jl",
          authors = "#",
-         clean = true,
-         doctest = false,
          modules = [NeuralPDE],
-         strict = [
-             :doctest,
-             :linkcheck,
-             :parse_error,
-             :example_block,
-             # Other available options are
-             # :autodocs_block, :cross_references, :docs_block, :eval_block, :example_block, :footnote, :meta_block, :missing_docs, :setup_block
-         ],
-         format = Documenter.HTML(analytics = "UA-90474609-3",
-                                  assets = ["assets/favicon.ico"],
+         clean = true, doctest = false, linkcheck = true,
+         warnonly = [:missing_docs, :example_block],
+         format = Documenter.HTML(assets = ["assets/favicon.ico"],
                                   canonical = "https://docs.sciml.ai/NeuralPDE/stable/"),
          pages = pages)
 

docs/src/examples/Lotka_Volterra_BPINNs.md

@@ -46,7 +46,7 @@ tspan = (0.0, 6.0)
 prob = ODEProblem(lotka_volterra, u0, tspan, p)
 
 ```
-With the [`saveat` argument](https://docs.sciml.ai/latest/basics/common_solver_opts/) we can specify that the solution is stored only at `saveat` time units(default saveat=1 / 50.0).
+With the [`saveat` argument](https://docs.sciml.ai/DiffEqDocs/stable/basics/common_solver_opts/) we can specify that the solution is stored only at `saveat` time units(default saveat=1 / 50.0).
 
 ```julia
 # Plot solution got by Standard DifferentialEquations.jl ODE solver

docs/src/examples/linear_parabolic.md

@@ -81,10 +81,14 @@ sym_prob = symbolic_discretize(pdesystem, discretization)
 pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
 bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions
 
+global iteration = 0
 callback = function (p, l)
-    println("loss: ", l)
-    println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
-    println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
+    if iteration % 10 == 0
+        println("loss: ", l)
+        println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
+        println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
+    end
+    global iteration += 1
     return false
 end
 

docs/src/examples/nonlinear_elliptic.md

@@ -95,11 +95,15 @@ pde_inner_loss_functions = sym_prob.loss_functions.pde_loss_functions
 bcs_inner_loss_functions = sym_prob.loss_functions.bc_loss_functions[1:6]
 aprox_derivative_loss_functions = sym_prob.loss_functions.bc_loss_functions[7:end]
 
+global iteration = 0
 callback = function (p, l)
-    println("loss: ", l)
-    println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
-    println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
-    println("der_losses: ", map(l_ -> l_(p), aprox_derivative_loss_functions))
+    if iteration % 10 == 0
+        println("loss: ", l)
+        println("pde_losses: ", map(l_ -> l_(p), pde_inner_loss_functions))
+        println("bcs_losses: ", map(l_ -> l_(p), bcs_inner_loss_functions))
+        println("der_losses: ", map(l_ -> l_(p), aprox_derivative_loss_functions))
+    end
+    global iteration += 1
     return false
 end
 

docs/src/index.md

@@ -15,7 +15,7 @@ networks which both approximate physical laws and real data simultaniously.
   - Integrated logging suite for handling connections to TensorBoard.
   - Handling of (partial) integro-differential equations and various stochastic equations.
   - Specialized forms for solving `ODEProblem`s with neural networks.
-  - Compatibility with [Flux.jl](https://docs.sciml.ai/Flux.jl/stable/) and [Lux.jl](https://docs.sciml.ai/Lux/stable/).
+  - Compatibility with [Flux.jl](https://fluxml.ai/) and [Lux.jl](https://lux.csail.mit.edu/).
     for all the GPU-powered machine learning layers available from those libraries.
   - Compatibility with [NeuralOperators.jl](https://docs.sciml.ai/NeuralOperators/stable/) for
     mixing DeepONets and other neural operators (Fourier Neural Operators, Graph Neural Operators,
@@ -132,32 +132,19 @@ Pkg.status(; mode = PKGMODE_MANIFEST) # hide
 </details>
 ```
 
-```@raw html
-You can also download the 
-<a href="
-```
-
 ```@eval
 using TOML
+using Markdown
 version = TOML.parse(read("../../Project.toml", String))["version"]
 name = TOML.parse(read("../../Project.toml", String))["name"]
-link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
-       "/assets/Manifest.toml"
-```
-
-```@raw html
-">manifest</a> file and the
-<a href="
-```
-
-```@eval
-using TOML
-version = TOML.parse(read("../../Project.toml", String))["version"]
-name = TOML.parse(read("../../Project.toml", String))["name"]
-link = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
-       "/assets/Project.toml"
-```
-
-```@raw html
-">project</a> file.
+link_manifest = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+                "/assets/Manifest.toml"
+link_project = "https://github.com/SciML/" * name * ".jl/tree/gh-pages/v" * version *
+               "/assets/Project.toml"
+Markdown.parse("""You can also download the
+[manifest]($link_manifest)
+file and the
+[project]($link_project)
+file.
+""")
 ```

src/pinn_types.jl

@@ -66,7 +66,7 @@ methodology.
   should only be used to more directly impose functional information in the training problem,
   for example imposing the boundary condition by the test function formulation.
 * `adaptive_loss`: the choice for the adaptive loss function. See the
-  [adaptive loss page](@id adaptive_loss) for more details. Defaults to no adaptivity.
+  [adaptive loss page](@ref adaptive_loss) for more details. Defaults to no adaptivity.
 * `additional_loss`: a function `additional_loss(phi, θ, p_)` where `phi` are the neural
   network trial solutions, `θ` are the weights of the neural network(s), and `p_` are the
   hyperparameters of the `OptimizationProblem`. If `param_estim = true`, then `θ` additionally