Reconstructing Rayleigh-Benard flows out of temperature-only measurements using Physics-Informed Neural Networks
This addresses flow reconstruction for fluid dynamics researchers, but it is incremental as it builds on existing PINN methods for a specific application.
The study tackled reconstructing turbulent Rayleigh-Benard flows using only temperature data with Physics-Informed Neural Networks (PINNs), finding that PINNs achieve high precision comparable to nudging at low Rayleigh numbers and outperform nudging at high Rayleigh numbers with dense data, but performance degrades with sparse data.
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.