13 citations · 20 across the 7 of their papers we have counts for
7 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…
SnapPINN: Pressure and Energy Dissipation Reconstruction from a Sparse and Noisy Velocity Snapshot
Robin Barta, Christian Bauer, Gholamhossein Bagheri
Reconstructing pressure and turbulence quantities from experimental velocity measurements is challenging, especially without time-resolved data. Furthermore, limitations such as lo…
How is the free surface influence transported in turbulent open channel flows?
Yoshiyuki Sakai, Christian Bauer
We investigate how the influence of a free surface is transported in turbulent open channel flow by analysing matched open- and closed-channel direct numerical simulations up to $R…
Relaminarization of turbulent pipe flow induced by streamwise traveling wave wall transpiration and its scaling
Christian Bauer, Claus Wagner
In technical applications, more than 90\% of the energy required to pump the fluids through pipes is dissipated by turbulence near the wall. In this respect, streamwise traveling w…
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…