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dc.contributor.authorDepina, Ivan
dc.contributor.authorJain, Saket
dc.contributor.authorValsson, Sigurdur Mar
dc.contributor.authorGotovaca, Hrvoje
dc.date.accessioned2021-10-13T07:52:27Z
dc.date.available2021-10-13T07:52:27Z
dc.date.created2021-10-12T09:21:26Z
dc.date.issued2021
dc.identifier.issn1749-9518
dc.identifier.urihttps://hdl.handle.net/11250/2789511
dc.description.abstractThis paper investigates the application of Physics-Informed Neural Networks (PINNs) to inverse problems in unsaturated groundwater flow. PINNs are applied to the types of unsaturated groundwater flow problems modelled with the Richards partial differential equation and the van Genuchten constitutive model. The inverse problem is formulated here as a problem with known or measured values of the solution to the Richards equation at several spatio-temporal instances, and unknown values of solution at the rest of the problem domain and unknown parameters of the van Genuchten model. PINNs solve inverse problems by reformulating the loss function of a deep neural network such that it simultaneously aims to satisfy the measured values and the unknown values at a set of collocation points distributed across the problem domain. The novelty of the paper originates from the development of PINN formulations for the Richards equation that requires training of a single neural network. The results demonstrate that PINNs are capable of efficiently solving the inverse problem with relatively accurate approximation of the solution to the Richards equation and estimates of the van Genuchten model parameters.en_US
dc.language.isoengen_US
dc.rightsCC BY 4.0*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPhysics-informeden_US
dc.subjectNeuralen_US
dc.subjectNetworken_US
dc.subjectRichardsen_US
dc.subjectUnsaturateden_US
dc.subjectInverseen_US
dc.subjectInfiltrationen_US
dc.subjectGroundwateren_US
dc.titleApplication of physics-informed neural networks to inverse problems in unsaturated groundwater flowen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2021 The Authors.en_US
dc.subject.nsiVDP::Teknologi: 500en_US
dc.source.journalGeorisk: Assessment and Management of Risk for Engineered Systems and Geohazardsen_US
dc.identifier.doi10.1080/17499518.2021.1971251
dc.identifier.cristin1945071
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Except where otherwise noted, this item's license is described as CC BY 4.0