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- W4385292718 abstract "Differentiable fluid simulators are increasingly demonstrating value as useful tools for developing data-driven models in computational fluid dynamics (CFD). Differentiable turbulence, or the end-to-end training of machine learning (ML) models embedded in CFD solution algorithms, captures both the generalization power and limited upfront cost of physics-based simulations, and the flexibility and automated training of deep learning methods. We develop a framework for integrating deep learning models into a generic finite element numerical scheme for solving the Navier-Stokes equations, applying the technique to learn a sub-grid scale closure using a multi-scale graph neural network. We demonstrate the method on several realizations of flow over a backwards-facing step, testing on both unseen Reynolds numbers and new geometry. We show that the learned closure can achieve accuracy comparable to traditional large eddy simulation on a finer grid that amounts to an equivalent speedup of 10x. As the desire and need for cheaper CFD simulations grows, we see hybrid physics-ML methods as a path forward to be exploited in the near future." @default.
- W4385292718 created "2023-07-27" @default.
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- W4385292718 date "2023-07-25" @default.
- W4385292718 modified "2023-09-25" @default.
- W4385292718 title "Differentiable Turbulence II" @default.
- W4385292718 doi "https://doi.org/10.48550/arxiv.2307.13533" @default.
- W4385292718 hasPublicationYear "2023" @default.
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