• Corpus ID: 232068635

Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling

@inproceedings{Takeishi2021PhysicsIntegratedVA,
  title={Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling},
  author={Naoya Takeishi and Alexandros Kalousis},
  booktitle={NeurIPS},
  year={2021}
}
Integrating physics models within machine learning holds considerable promise toward learning robust models with improved interpretability and abilities to extrapolate. In this work, we focus on the integration of incomplete physics models into deep generative models, variational autoencoders (VAEs) in particular. A key technical challenge is to strike a balance between the incomplete physics model and the learned components (i.e., neural nets) of the complete model, in order to ensure that the… 
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