Fusing Physical Knowledge with Neural Networks’ Flexibility

February 22, 2023 / ML Cluster*

[Picture: Franz-Georg Stämmele]

Diffusion is an ubiquitous process in nature. Water diffuses in soil when we water our plants. Heat diffuses into a steak when we fry it. And chemical reactants diffuse through the test strip when we take a coronavirus self-test. However, applying diffusion equations from physics often requires simplifying assumptions to estimate unknown factors, such as diffusion rates. Such unknown factors can be learnt from data by physics-aware machine learning and first approaches have generated promising results. However, the mathematics behind current physics-aware machine learning models is mostly incomprehensible to scientists, which hinders knowledge discovery.

Full article on www.machinelearningforscience.de

*Authors: Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz

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