Regular Article - Flowing Matter
Assimilation of statistical data into turbulent flows using physics-informed neural networks
Facultad de Ciencias Exactas y Naturales, Departamento de Física, Ciudad Universitaria, Universidad de Buenos Aires, 1428, Buenos Aires, Argentina
2 Instituto de Física del Plasma (INFIP), Ciudad Universitaria, CONICET - Universidad de Buenos Aires, 1428, Buenos Aires, Argentina
3 Université Grenoble Alpes, CNRS, Grenoble-INP, LEGI, 38000, Grenoble, France
4 Departmento de Ingeniería, Universidad de San Andrés, Buenos Aires, Argentina
Accepted: 12 February 2023
Published online: 9 March 2023
When modeling turbulent flows, it is often the case that information on the forcing terms or the boundary conditions is either not available or overly complicated and expensive to implement. Instead, some flow features, such as the mean velocity profile or its statistical moments, may be accessible through experiments or observations. We present a method based on physics-informed neural networks to assimilate a given set of conditions into turbulent states. The physics-informed method helps the final state approximate a valid flow. We show examples of different statistical conditions that can be used to prepare states, motivated by experimental and atmospheric problems. Lastly, we show two ways of scaling the resolution of the prepared states. One is through the use of multiple and parallel neural networks. The other uses nudging, a synchronization-based data assimilation technique that leverages the power of specialized numerical solvers.
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