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DEEPDIP

Discovering deep physics models with differentiable programming

Many physics models feature terms that are either partially unknown or too expensive to simulate. Discovering effective equations that represent such terms is a fundamental challenge in computational science. Multi-scale models are a prominent example: the large-scale behaviour is of main interest, but this cannot be obtained without resolving the fine scales. A well-known example occurs in climate models, which rely on the effect of clouds for accurate forecasts, but simulating clouds individually is computationally intractable. We propose a new software framework to extend generic physics models with data-driven neural networks (NNs) that represent the effect of small scales on large scales. The framework will use differentiable programming, allowing to couple multi-scale models and NNs while embedded in a learning environment. We test our framework on turbulent fluid flows. In particular, we develop new differentiable wind-turbine wake models, to be used for optimal control of wind farms.

Participating organisations

CWI
Netherlands eScience Center
Otto-von-Guericke University Magdeburg
Delft University of Technology

Team

BS
Benjamin Sanderse
SA
Syver Døving Agdestein
PhD candidate
Centrum Wiskunde & Informatica
TvG
Toby van Gastelen
PhD candidate
CWI
AB
Andrea Beck
Advisor
University of Magdeburg
JvW
Jan-Willem van Wingerden
Advisor
Delft University of Technology
Niels  Drost
Niels Drost
Programme Manager
Netherlands eScience Center