Estimating Bottom Topography in Shallow Water Flows

Autores
Pancotto, L.; Clark Di Leoni, Patricio
Año de publicación
2026
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We present two methods to estimate bottom topography in a shallow water flow using only surface deformation measurements. One is based on Physics-Informed Neural Networks (PINNs) and the other on the Adjoint State Method. We test both methods using synthetic data in 1D and 2D cases. Both are able to successfully reconstruct not only the bottom topography but also the surface velocity. Both also show robustness against noise and data sparsity up to reasonable levels.
Fil: Pancotto, L.. University Johns Hopkins; Estados Unidos. Universidad de San Andrés; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Clark Di Leoni, Patricio. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
Bathymetry
Machine Learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/290339
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Estimating Bottom Topography in Shallow Water FlowsPancotto, L.Clark Di Leoni, PatricioBathymetryMachine Learninghttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1We present two methods to estimate bottom topography in a shallow water flow using only surface deformation measurements. One is based on Physics-Informed Neural Networks (PINNs) and the other on the Adjoint State Method. We test both methods using synthetic data in 1D and 2D cases. Both are able to successfully reconstruct not only the bottom topography but also the surface velocity. Both also show robustness against noise and data sparsity up to reasonable levels.Fil: Pancotto, L.. University Johns Hopkins; Estados Unidos. Universidad de San Andrés; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Clark Di Leoni, Patricio. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaWiley2026-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/290339Pancotto, L.; Clark Di Leoni, Patricio; Estimating Bottom Topography in Shallow Water Flows; Wiley; Journal of Geophysical Research: Machine Learning and Computation; 3; 2; 4-2026; 1-212993-5210CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JH001088info:eu-repo/semantics/altIdentifier/doi/10.1029/2025JH001088info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2026-08-25T14:36:13Zoai:ri.conicet.gov.ar:11336/290339instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982026-08-25 14:36:14.033CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Estimating Bottom Topography in Shallow Water Flows
title Estimating Bottom Topography in Shallow Water Flows
spellingShingle Estimating Bottom Topography in Shallow Water Flows
Pancotto, L.
Bathymetry
Machine Learning
title_short Estimating Bottom Topography in Shallow Water Flows
title_full Estimating Bottom Topography in Shallow Water Flows
title_fullStr Estimating Bottom Topography in Shallow Water Flows
title_full_unstemmed Estimating Bottom Topography in Shallow Water Flows
title_sort Estimating Bottom Topography in Shallow Water Flows
dc.creator.none.fl_str_mv Pancotto, L.
Clark Di Leoni, Patricio
author Pancotto, L.
author_facet Pancotto, L.
Clark Di Leoni, Patricio
author_role author
author2 Clark Di Leoni, Patricio
author2_role author
dc.subject.none.fl_str_mv Bathymetry
Machine Learning
topic Bathymetry
Machine Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.3
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We present two methods to estimate bottom topography in a shallow water flow using only surface deformation measurements. One is based on Physics-Informed Neural Networks (PINNs) and the other on the Adjoint State Method. We test both methods using synthetic data in 1D and 2D cases. Both are able to successfully reconstruct not only the bottom topography but also the surface velocity. Both also show robustness against noise and data sparsity up to reasonable levels.
Fil: Pancotto, L.. University Johns Hopkins; Estados Unidos. Universidad de San Andrés; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Clark Di Leoni, Patricio. Universidad de San Andrés; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description We present two methods to estimate bottom topography in a shallow water flow using only surface deformation measurements. One is based on Physics-Informed Neural Networks (PINNs) and the other on the Adjoint State Method. We test both methods using synthetic data in 1D and 2D cases. Both are able to successfully reconstruct not only the bottom topography but also the surface velocity. Both also show robustness against noise and data sparsity up to reasonable levels.
publishDate 2026
dc.date.none.fl_str_mv 2026-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/290339
Pancotto, L.; Clark Di Leoni, Patricio; Estimating Bottom Topography in Shallow Water Flows; Wiley; Journal of Geophysical Research: Machine Learning and Computation; 3; 2; 4-2026; 1-21
2993-5210
CONICET Digital
CONICET
url http://hdl.handle.net/11336/290339
identifier_str_mv Pancotto, L.; Clark Di Leoni, Patricio; Estimating Bottom Topography in Shallow Water Flows; Wiley; Journal of Geophysical Research: Machine Learning and Computation; 3; 2; 4-2026; 1-21
2993-5210
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2025JH001088
info:eu-repo/semantics/altIdentifier/doi/10.1029/2025JH001088
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.265058

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