{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li S"],"funding":["National Natural Science Foundation of China"],"pagination":["100266"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11533095"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25"],"pubmed_abstract":["Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A "],"journal":["Water research X"],"pubmed_title":["Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning."],"pmcid":["PMC11533095"],"funding_grant_id":["51978493"],"pubmed_authors":["Xin K","Li S","Tian W","Tao T","Yan H","Zeng W"],"additional_accession":[]},"is_claimable":false,"name":"Modeling transient mixed flows in sewer systems with data fusion via physics-informed machine learning.","description":"Transitions between free-surface and pressurized flows, known as transient mixed flows, have posed significant challenges in urban drainage systems (UDS), e.g., pipe bursts, road collapses, and geysers. However, traditional mechanistic modeling for mixed flows is challenged by the difficult integration of multi-source data, complex equation forms for the discovery of dynamic processes, and high computational demands. In response, we proposed a data-driven model, TMF-PINN, which utilizes a Physics-Informed Neural Network (PINN) to simulate and invert Transient Mixed Flow (TMF) in sewer networks. This model integrates experimental data, simulation results and Partial Differential Equations (PDEs) into its loss function, leveraging the extensive data available in smart urban water systems. A ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Dec","modification":"2026-07-16T09:37:02.7Z","creation":"2025-04-04T07:28:01.298Z"},"accession":"S-EPMC11533095","cross_references":{"pubmed":["39497789"],"doi":["10.1016/j.wroa.2024.100266"]}}