  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.
