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

View publication

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.

Related readings and updates.

Exploring Empty Spaces: Human-in-the-Loop Data Augmentation

March 26, 2025research area Human-Computer Interaction, research area Tools, Platforms, Frameworksconference CHI

Data augmentation is crucial to make machine learning models more robust and safe. However, augmenting data can be challenging as it requires generating diverse data points to rigorously evaluate model behavior on edge cases and mitigate potential harms. Creating high-quality augmentations that cover these "unknown unknowns" is a time- and creativity-intensive task. In this work, we introduce Amplio, an interactive tool to help practitioners…
Read more

Robust Hybrid Learning With Expert Augmentation

March 13, 2023research area Methods and AlgorithmsTransactions on Machine Learning Research (TMLR)

Hybrid modelling reduces the misspecification of expert models by combining them with machine learning (ML) components learned from data. Similarly to many ML algorithms, hybrid model performance guarantees are limited to the training distribution. Leveraging the insight that the expert model is usually valid even outside the training domain, we overcome this limitation by introducing a hybrid data augmentation strategy termed \textit{expert…
Read more