Automated Dissipation Control for Turbulence Simulation with Shell Models

• URL: http://arxiv.org/abs/2201.02485v1
• Date: Fri, 7 Jan 2022 15:03:52 GMT
• Title: Automated Dissipation Control for Turbulence Simulation with Shell Models
• Authors: Ann-Kathrin Dombrowski, Klaus-Robert M\"uller, Wolf Christian M\"uller
• Abstract summary: The application of machine learning (ML) techniques, especially neural networks, has seen tremendous success at processing images and language. In this work we construct a strongly simplified representation of turbulence by using the Gledzer-Ohkitani-Yamada shell model. We propose an approach that aims to reconstruct statistical properties of turbulence such as the self-similar inertial-range scaling.
• Score: 1.675857332621569
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The application of machine learning (ML) techniques, especially neural networks, has seen tremendous success at processing images and language. This is because we often lack formal models to understand visual and audio input, so here neural networks can unfold their abilities as they can model solely from data. In the field of physics we typically have models that describe natural processes reasonably well on a formal level. Nonetheless, in recent years, ML has also proven useful in these realms, be it by speeding up numerical simulations or by improving accuracy. One important and so far unsolved problem in classical physics is understanding turbulent fluid motion. In this work we construct a strongly simplified representation of turbulence by using the Gledzer-Ohkitani-Yamada (GOY) shell model. With this system we intend to investigate the potential of ML-supported and physics-constrained small-scale turbulence modelling. Instead of standard supervised learning we propose an approach that aims to reconstruct statistical properties of turbulence such as the self-similar inertial-range scaling, where we could achieve encouraging experimental results. Furthermore we discuss pitfalls when combining machine learning with differential equations.

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