Lagrangian Measurements and Physics-Informed Neural Network for Rayleigh-Bénard Flow Reconstruction
Résumé
The aim of this work is to reconstruct the temperature field from Lagrangian measurements in a turbulent Rayleigh-Bénard flow using a Physics Informed Neural Network. First, the spatial concentration of particles in the experimental dataset is examined using a 3D Voronoï analysis with a view to merging the experimental and numerical databases.
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