# Blending Diverse Physical Priors with Neural Networks

@article{Ba2019BlendingDP, title={Blending Diverse Physical Priors with Neural Networks}, author={Yunhao Ba and Guangyuan Zhao and Achuta Kadambi}, journal={ArXiv}, year={2019}, volume={abs/1910.00201} }

Machine learning in context of physical systems merits a re-examination of the learning strategy. In addition to data, one can leverage a vast library of physical prior models (e.g. kinematics, fluid flow, etc) to perform more robust inference. The nascent sub-field of \emph{physics-based learning} (PBL) studies the blending of neural networks with physical priors. While previous PBL algorithms have been applied successfully to specific tasks, it is hard to generalize existing PBL methods to a…

## 18 Citations

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