{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:47:44Z","timestamp":1776181664412,"version":"3.50.1"},"reference-count":127,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T00:00:00Z","timestamp":1686960000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation (NSF)","award":["NSF CPS-2038984"],"award-info":[{"award-number":["NSF CPS-2038984"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.<\/jats:p>","DOI":"10.3390\/a16060305","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T01:59:51Z","timestamp":1687139991000},"page":"305","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Physics-Informed Deep Learning for Traffic State Estimation: A Survey and the Outlook"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2925-7697","authenticated-orcid":false,"given":"Xuan","family":"Di","sequence":"first","affiliation":[{"name":"Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA"},{"name":"Data Science Institute, Columbia University, New York, NY 10027, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4298-9358","authenticated-orcid":false,"given":"Rongye","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0465-8550","authenticated-orcid":false,"given":"Zhaobin","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1174-8386","authenticated-orcid":false,"given":"Yongjie","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","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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