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

Understand

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.