Reconstructing Rayleigh-Benard flows out of temperature-only measurements using Physics-Informed Neural Networks

by   Patricio Clark Di Leoni, et al.
Universidad de San Andrés

We investigate the capabilities of Physics-Informed Neural Networks (PINNs) to reconstruct turbulent Rayleigh-Benard flows using only temperature information. We perform a quantitative analysis of the quality of the reconstructions at various amounts of low-passed-filtered information and turbulent intensities. We compare our results with those obtained via nudging, a classical equation-informed data assimilation technique. At low Rayleigh numbers, PINNs are able to reconstruct with high precision, comparable to the one achieved with nudging. At high Rayleigh numbers, PINNs outperform nudging and are able to achieve satisfactory reconstruction of the velocity fields only when data for temperature is provided with high spatial and temporal density. When data becomes sparse, the PINNs performance worsens, not only in a point-to-point error sense but also, and contrary to nudging, in a statistical sense, as can be seen in the probability density functions and energy spectra.


Magnetohydrodynamics with Physics Informed Neural Operators

We present the first application of physics informed neural operators, w...

Robustness of Physics-Informed Neural Networks to Noise in Sensor Data

Physics-Informed Neural Networks (PINNs) have been shown to be an effect...

MR-Based Electrical Property Reconstruction Using Physics-Informed Neural Networks

Electrical properties (EP), namely permittivity and electric conductivit...

Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data

High-fidelity reconstruction of fluids from sparse multiview RGB videos ...

Please sign up or login with your details

Forgot password? Click here to reset