Web: http://arxiv.org/abs/2010.04456

May 11, 2022, 1:11 a.m. | Yuan Yin, Vincent Le Guen, Jérémie Dona, Emmanuel de Bézenac, Ibrahim Ayed, Nicolas Thome, Patrick Gallinari

cs.LG updates on arXiv.org arxiv.org

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 the APHYNITY framework, a principled approach for augmenting
incomplete physical dynamics described by differential equations with deep
data-driven models. It consists in decomposing the dynamics into two
components: a physical …

arxiv deep dynamics forecasting ml models networks

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