Turbulence forecasting via Neural ODE

TL;DR

This paper introduces a Neural ODE-based machine learning method for modeling turbulence dissipation, capturing turbulence phenomenology without predefined models, and demonstrating superior performance over existing approaches.

Contribution

The paper presents a novel Neural ODE methodology for turbulence modeling that learns turbulence phenomenology de novo, without relying on pre-specified model forms.

Findings

Outperforms state-of-the-art turbulence modeling approaches

Effectively captures turbulence dissipation phenomenology

Provides a new ML framework for turbulence modeling

Abstract

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.