13 citations · 20 across the 5 of their papers we have counts for
5 papers
An improved periodic activation for PINNs reconstructing convective flows
Michael Mommert, Marie-Christine Volk, Christian Bauer
Architectures with periodic activation functions have already been shown to be beneficial in comparison to monotonic counterparts for a wide range of applications of physics-inform…
Temperature and pressure reconstruction in turbulent Rayleigh-Bénard convection by Lagrangian velocities using PINN
R. Barta, M. -C. Volk, C. Bauer +2
Velocity, pressure, and temperature are the key variables for understanding thermal convection, and measuring them all is a complex task. In this paper, we demonstrate a method to…
A PINN Methodology for Temperature Field Reconstruction in the PIV Measurement Plane: Case of Rayleigh-Bénard Convection
Marie-Christine Volk, Anne Sergent, Didier Lucor +3
We present a method to infer temperature fields from stereo particle-image velocimetry (PIV) data in turbulent Rayleigh-Bénard convection (RBC) using Physics-informed neural networ…
Curvature-based energy spectra revealing flow regime changes in Rayleigh-Bénard convection
Michael Mommert, Philipp Bahavar, Robin Barta +3
We use the local curvature derived from velocity vector fields or particle tracks as a surrogate for structure size to compute curvature-based energy spectra. An application to hom…
Periodically activated physics-informed neural networks for assimilation tasks for three-dimensional Rayleigh-Bénard convection
Michael Mommert, Robin Barta, Christian Bauer +2
We apply physics-informed neural networks to three-dimensional Rayleigh-Bénard convection in a cubic cell with a Rayleigh number of Ra = 10^6 and a Prandtl number of Pr = 0.7 to as…