22.12

Main notes

The new version of DeepPhysX brings a lot of changes with the data storage and the data flow between components.
Instead of using Numpy tensors referenced as input and output of neural networks, data is now streamed on a SQL Database that can contain any data field.
This allows to easily design new data flows and to interface with more complex network architectures and learning algorithms.

The SQL Database features are implemented in an external Python library - SimulationSimpleDatabase - that must be installed to use this release.
--> quick install with pip install SimulationSimpleDatabase

Changes

Network

• the Network object now has three variables that must be set to define the data field names: net_fields for the input data fields (should match the data field names defined in the Environment), opt_fields for the data used for the optimization process, pred_fields for the data that will be returned to the Environment;
• some API updates for TorchNetwork and TorchNetworkConfig (see examples/tutorial for further details).

  from DeepPhysX.Torch.Network.TorchNetwork import TorchNetwork
  
  class MyNetwork(TorchNetwork):
    
        def __init__(self, config):
            BaseNetwork.__init__(self, config)
            self.net_fields = ['input']
            self.opt_fields = ['ground_truth']
            self.pred_fields = ['prediction']

22.06

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.
This layer ensures the compatibility with PyTorch.

This version is the first stable release of the project.
It provides a Torch package with a documentation page and examples.

Features

• Compatibility between the DPX API ant PyTorch to easily define new Network architectures for DPX environment;
• Already implemented architectures: FC, UNet.