Augmenting physical models with deep networks for complex dynamics forecasting
Augmenting physical models with deep networks for complex dynamics forecasting
Forecasting complex dynamical phenomena in settings where only partial knowledge of their dynamics is available is a prevalent problem across various scientific fields. While purely data-driven approaches are arguably insufficient in this context, standard physical modeling based approaches tend to be over-simplistic, inducing non-negligible errors. In this work, we introduce …