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Turbulence forecasting via Neural ODE

Gavin D. Portwood; Peetak P. Mitra; Mateus Dias Ribeiro; Tan Minh Nguyen; Balasubramanya T. Nadiga; Juan A. Saenz; Michael Chertkov; Animesh Garg; Anima Anandkumar; Andreas Dengel; Richard Baraniuk; David P. Schmidt
In: Second Workshop on Machine Learning and the Physical Sciences. Machine Learning and the Physical Sciences Workshop (ML4PS-2019), located at NeurIPS 2019, December 14, Vancouver, Canada, arXiv, 11/2019.


Fluid turbulence is characterized by strong coupling across a broad range of scales. Furthermore, besides the usual local cascades, such coupling may extend to interactions that are non-local in scale-space. As such the computational demands associated with explicitly resolving the full set of scales and their interactions, as in the Direct Numerical Simulation (DNS) of the Navier-Stokes equations, in most problems of practical interest are so high that reduced modeling of scales and interactions is required before further progress can be made. While popular reduced models are typically based on phenomenological modeling of relevant turbulent processes, recent advances in machine learning techniques have energized efforts to further improve the accuracy of such reduced models. In contrast to such efforts that seek to improve an existing turbulence model, we propose a machine learning(ML) methodology that captures, de novo, underlying turbulence phenomenology without a pre-specified model form. To illustrate the approach, we consider transient modeling of the dissipation of turbulent kinetic energy, a fundamental turbulent process that is central to a wide range of turbulence models using a Neural ODE approach. After presenting details of the methodology, we show that this approach outperforms state-of-the-art approaches.

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