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- Team Nabla's repository (private access for eScience members only)
- Slack channel
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.
- Automatic differentiation from scratch. eScience Center blog.
- Recent articles:
- MSc thesis:
- Github documentation:
- IncompressibleNavierStokes.jl
- For example, the discretization is outlined here
- IncompressibleNavierStokes.jl
- General CFD:
- Computational methods for fluid dynamics, Ferziger & Peric.
- Veldman lecture notes on CFD.
- MUSCLE3:
- Neural differential equations:
- On neural differential equations. Textbook by Dr. Patrick Kidger.
- Interactive tutorials on neural closure models
- PDE benchmarking:
- PDEBENCH: An Extensive Benchmark for Scientific Machine Learning. arXiv
- Includes code repo
- Julia package
SciMLBenchmarks.jl
- PDEBENCH: An Extensive Benchmark for Scientific Machine Learning. arXiv
- Announcement on CWI website
- NNs: Neural Networks
- CFD: Computational Fluid Dynamics
