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Representing The Solution Operator For The Navier-Stokes Equation

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TURW04 - Wall-bounded turbulence: beyond current boundaries

For several hundred years our models of continuum phenomena have been constructed from basic primitives which directly express balance laws and constitutive models. In this talk we explore the possibility of using ideas from machine learning to represent the predictions of these balance laws in new forms. These new representations appear to allow for cheap computational primitives, in certain regimes, which outperform existing solvers based on discretization of the balance laws. These ideas are exemplified in the context of 2D isoptropic incompressible turbulent flow.

This talk is part of the Isaac Newton Institute Seminar Series series.

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