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We then show how recently introduced adaptive techniques for spectral methods can be integrated into PINN-based PDE solvers to obtain numerical solutions of unbounded domain problems that cannot be efficiently approximated by standard PINNs. Through a number of examples, we demonstrate the advantages of the proposed spectrally adapted PINNs in solving PDEs and estimating model parameters from noisy observations in unbounded domains.<\/jats:p>","DOI":"10.1088\/2632-2153\/acd0a1","type":"journal-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T22:40:27Z","timestamp":1682548827000},"page":"025024","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Spectrally adapted physics-informed neural networks for solving unbounded domain problems"],"prefix":"10.1088","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2116-4712","authenticated-orcid":true,"given":"Mingtao","family":"Xia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1700-1897","authenticated-orcid":true,"given":"Lucas","family":"B\u00f6ttcher","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0785-6349","authenticated-orcid":true,"given":"Tom","family":"Chou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2023,5,26]]},"reference":[{"key":"mlstacd0a1bib1","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0893-6080(91)90009-T","article-title":"Approximation capabilities of multilayer feedforward networks","volume":"4","author":"Hornik","year":"1991","journal-title":"Neural Netw."},{"key":"mlstacd0a1bib2","article-title":"Minimum width for universal approximation","author":"Park","year":"2020"},{"key":"mlstacd0a1bib3","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","article-title":"Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations","volume":"378","author":"Raissi","year":"2019","journal-title":"J. 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