Robust Hybrid Learning With Expert Augmentation

Hybrid modelling reduces the misspecification of expert models by combining
them with machine learning (ML) components learned from data. Like for 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 augmentation}. Based on
a probabilistic formalization of hybrid modelling, we show why expert
augmentation improves generalization. Finally, we validate the practical
benefits of augmented hybrid models on a set of controlled experiments,
modelling dynamical systems described by ordinary and partial differential
equations.