research area Methods and Algorithmsconference NeurIPS
content type paperpublished November 2022

Improving Generalization with Physical Equations

In collaboration with Harvard University, University of Liège

AuthorsAntoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen

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This paper was accepted at the workshop "Machine Learning 4 Physical Sciences" at NeurIPS 2022.

Hybrid modelling reduces the misspecification of expert physical models with a machine learning (ML) component learned from data. Similarly to many ML algorithms, hybrid model performance guarantees are limited to the training distribution. To address this limitation, here we introduce a hybrid data augmentation strategy, termed expert augmentation. Based on a probabilistic formalization of hybrid modelling, we demonstrate that expert augmentation improves generalization. We validate the practical benefits of expert augmentation on a set of simulated and real-world systems described by classical mechanics.

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