Coercing Machine Learning to Output Physically Accurate Results
This addresses the issue of physically inaccurate outputs in ML models for applications like computer graphics and simulation, though it is an incremental improvement by modifying training to incorporate existing constraints.
The paper tackles the problem of machine learning models producing physically inaccurate outputs by integrating physical constraints directly into the network training, rather than as a postprocess. This approach significantly reduces stretching and compression energies in a cloth mesh prediction task, producing highly improved results.
Many machine/deep learning artificial neural networks are trained to simply be interpolation functions that map input variables to output values interpolated from the training data in a linear/nonlinear fashion. Even when the input/output pairs of the training data are physically accurate (e.g. the results of an experiment or numerical simulation), interpolated quantities can deviate quite far from being physically accurate. Although one could project the output of a network into a physically feasible region, such a postprocess is not captured by the energy function minimized when training the network; thus, the final projected result could incorrectly deviate quite far from the training data. We propose folding any such projection or postprocess directly into the network so that the final result is correctly compared to the training data by the energy function. Although we propose a general approach, we illustrate its efficacy on a specific convolutional neural network that takes in human pose parameters (joint rotations) and outputs a prediction of vertex positions representing a triangulated cloth mesh. While the original network outputs vertex positions with erroneously high stretching and compression energies, the new network trained with our physics prior remedies these issues producing highly improved results.