---
_id: '12791'
abstract:
- lang: eng
  text: 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.
acknowledgement: This project has received partial funding from the European Research
  Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme
  (Grant Agreement No. 882340))
article_number: '16'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Patricio
  full_name: Clark Di Leoni, Patricio
  last_name: Clark Di Leoni
- first_name: Lokahith N
  full_name: Agasthya, Lokahith N
  id: cd100965-0804-11ed-9c55-f4878ff4e877
  last_name: Agasthya
- first_name: Michele
  full_name: Buzzicotti, Michele
  last_name: Buzzicotti
- first_name: Luca
  full_name: Biferale, Luca
  last_name: Biferale
citation:
  ama: Clark Di Leoni P, Agasthya LN, Buzzicotti M, Biferale L. Reconstructing Rayleigh–Bénard
    flows out of temperature-only measurements using Physics-Informed Neural Networks.
    <i>The European Physical Journal E</i>. 2023;46(3). doi:<a href="https://doi.org/10.1140/epje/s10189-023-00276-9">10.1140/epje/s10189-023-00276-9</a>
  apa: Clark Di Leoni, P., Agasthya, L. N., Buzzicotti, M., &#38; Biferale, L. (2023).
    Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using
    Physics-Informed Neural Networks. <i>The European Physical Journal E</i>. Springer
    Nature. <a href="https://doi.org/10.1140/epje/s10189-023-00276-9">https://doi.org/10.1140/epje/s10189-023-00276-9</a>
  chicago: Clark Di Leoni, Patricio, Lokahith N Agasthya, Michele Buzzicotti, and
    Luca Biferale. “Reconstructing Rayleigh–Bénard Flows out of Temperature-Only Measurements
    Using Physics-Informed Neural Networks.” <i>The European Physical Journal E</i>.
    Springer Nature, 2023. <a href="https://doi.org/10.1140/epje/s10189-023-00276-9">https://doi.org/10.1140/epje/s10189-023-00276-9</a>.
  ieee: P. Clark Di Leoni, L. N. Agasthya, M. Buzzicotti, and L. Biferale, “Reconstructing
    Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed
    Neural Networks,” <i>The European Physical Journal E</i>, vol. 46, no. 3. Springer
    Nature, 2023.
  ista: Clark Di Leoni P, Agasthya LN, Buzzicotti M, Biferale L. 2023. Reconstructing
    Rayleigh–Bénard flows out of temperature-only measurements using Physics-Informed
    Neural Networks. The European Physical Journal E. 46(3), 16.
  mla: Clark Di Leoni, Patricio, et al. “Reconstructing Rayleigh–Bénard Flows out
    of Temperature-Only Measurements Using Physics-Informed Neural Networks.” <i>The
    European Physical Journal E</i>, vol. 46, no. 3, 16, Springer Nature, 2023, doi:<a
    href="https://doi.org/10.1140/epje/s10189-023-00276-9">10.1140/epje/s10189-023-00276-9</a>.
  short: P. Clark Di Leoni, L.N. Agasthya, M. Buzzicotti, L. Biferale, The European
    Physical Journal E 46 (2023).
date_created: 2023-04-02T22:01:11Z
date_published: 2023-03-20T00:00:00Z
date_updated: 2023-08-01T14:03:47Z
day: '20'
department:
- _id: CaMu
doi: 10.1140/epje/s10189-023-00276-9
external_id:
  arxiv:
  - '2301.07769'
  isi:
  - '000956387200001'
intvolume: '        46'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: ' https://doi.org/10.48550/arXiv.2301.07769'
month: '03'
oa: 1
oa_version: Preprint
publication: The European Physical Journal E
publication_identifier:
  eissn:
  - 1292-895X
  issn:
  - 1292-8941
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Reconstructing Rayleigh–Bénard flows out of temperature-only measurements using
  Physics-Informed Neural Networks
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 46
year: '2023'
...