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@RobinEnjalbert RobinEnjalbert released this 02 Dec 08:43
· 81 commits to master since this release
v22.06
44c5934

Main notes

The DPX project provides easy solutions to interface AI with numerical simulations.
The project provides a Core package with additional compatibility layers for external AI and simulations frameworks.
The project includes three main AI pipelines:

  • Generate a dataset with synthetic data from numerical simulations;
  • Train an artificial neural network with a synthetic dataset;
  • Use the prediction of trained networks in a numerical simulation.

This version is the first stable release of the project.
It provides a Core package with 2 corresponding SOFA & PyTorch compatible layers. It also provides a documentation page and examples with shared training data.

Features

Dataset

  • Automatic training dataset storage and loading with multiple files management;
  • Dataset shuffle and normalization;
  • Multiple dataset modes: Training, Validation, Prediction;
  • Customizable dataset fields.

Simulation

  • Data generation achieved by several simulations running in multiprocessing with a client-server architecture;
  • Operation with internal data, from the dataset or from the neural network;
  • Increased interactions with other components (dataset, neural network, visualizer);
  • Check the validity of the training data;
  • A visualization Factory to init, update and render the simulated objects (written with Vedo).

Network

  • Automatic storage and loading of networks during training;
  • Customizable data transformations at each step (forward pass, optimization, prediction apply);
  • Customizable optimization process with training data;
  • An analysis of the evolution of the training session (written with Tensorboard);
  • Already implemented architectures: FC, UNet.
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