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

Turbulence forecasting via Neural ODE