https://doi.org/10.1140/epje/s10189-023-00276-9
Regular Article - Flowing Matter
Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed Neural Networks
1
Departmento de Ingeniería, Universidad de San Andrés, Buenos Aires, Argentina
2
Department of Physics and INFN, University of Rome “Tor Vergata”, Rome, Italy
3
Institute of Science and Technology Austria, Am Campus 1, 3400, Klosterneuburg, Austria
Received:
12
January
2023
Accepted:
3
March
2023
Published online:
20
March
2023
We investigate the capabilities of Physics-Informed Neural Networks (PINNs) to reconstruct turbulent Rayleigh–Bénard 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.
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