---
res:
  bibo_abstract:
  - 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.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Patricio
      foaf_name: Clark Di Leoni, Patricio
      foaf_surname: Clark Di Leoni
  - foaf_Person:
      foaf_givenName: Lokahith N
      foaf_name: Agasthya, Lokahith N
      foaf_surname: Agasthya
      foaf_workInfoHomepage: http://www.librecat.org/personId=cd100965-0804-11ed-9c55-f4878ff4e877
  - foaf_Person:
      foaf_givenName: Michele
      foaf_name: Buzzicotti, Michele
      foaf_surname: Buzzicotti
  - foaf_Person:
      foaf_givenName: Luca
      foaf_name: Biferale, Luca
      foaf_surname: Biferale
  bibo_doi: 10.1140/epje/s10189-023-00276-9
  bibo_issue: '3'
  bibo_volume: 46
  dct_date: 2023^xs_gYear
  dct_identifier:
  - UT:000956387200001
  dct_isPartOf:
  - http://id.crossref.org/issn/1292-8941
  - http://id.crossref.org/issn/1292-895X
  dct_language: eng
  dct_publisher: Springer Nature@
  dct_title: Reconstructing Rayleigh–Bénard flows out of temperature-only measurements
    using Physics-Informed Neural Networks@
  fabio_hasPubmedId: '36939938'
...