DeepPhysX 22.6

A Python framework interfacing AI with numerical simulation.

DeepPhysX

Interfacing AI with simulation

The DeepPhysX project provides Python packages allowing users to easily interface their numerical simulations with learning algorithms.

DeepPhysX provides a Core package with no dependency on any simulation or AI framework. Then other packages are compatible with this Core and a specific simulation or AI framework:

• DeepPhysX_Sofa as simulation packages;
• DeepPhysX_Torch as AI packages.

Features

DeepPhysX is a full Python3 projects with 3 main features:

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

The full list of features is detailed in the documentation.

Quick install

The project was initially developed using SOFA as the simulation package and PyTorch as the AI framework. Thus, DeepPhysX is mainly designed for these frameworks, but obviously other frameworks can also be used. The packages corresponding to these frameworks will therefore be used for the default installation. For further instructions (dependencies, set up your installation config, developer mode), please refer to the documentation.

$ pip install DeepPhysX             # Install default package
$ pip install DeepPhysX_Sofa        # Install simulation package
$ pip install DeepPhysX_Torch       # Install AI package

Demos

DeepPhysX includes a set of detailed tutorials, examples and demos. Following this installation process to directly try the interactive demos:

$ mkdir DeepPhysX
$ cd DeepPhysX
$ git clone https://github.com/mimesis/deepphysx.git Core   # Make shure to clone this repository in 'DeepPhysX/Core'
$ cd Core
$ python3 config.py                                         # Answer 'yes' to install Torch package to launch examples
$ pip install .

Armadillo python3 demo.py armadillo	Beam python3 demo.py beam	Liver python3 demo.py liver