{"paper":{"title":"Physics Informed Neural Network-based Computational Method for Accelerating Time-Periodic Unsteady CFD Simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients.","cross_cats":["physics.flu-dyn"],"primary_cat":"physics.comp-ph","authors_text":"Atul Sharma, Harshita Agarwal, Lakshya Chaplot","submitted_at":"2026-05-18T12:54:23Z","abstract_excerpt":"Presently, there is a steady state approach in Computational fluid dynamics (CFD) to obtain a steady solution directly from the steady state governing equations. Whereas, for obtaining a time-periodic flow solution, the present unsteady governing equations-based CFD approach starts from an initial condition and requires a large computational time during the initial non-periodic transient phase before reaching the periodic state. For obtaining the periodic flow directly, without transient simulations that may not be of interest, our objective is to propose a Physics Informed Neural Network (PIN"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results demonstrate that the PINN-based periodic solver takes substantially less computational time to achieve almost same accuracy as that obtained by the traditional transient-to-periodic solver.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a neural network optimized solely over one time period using a physics-informed loss can converge to the correct periodic solution without any dependence on or simulation of the transient evolution from initial conditions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6534f003ad49821cedb2017ad309074b42bbf8d24b87a50eb3fec698773f4f25"},"source":{"id":"2605.18340","kind":"arxiv","version":1},"verdict":{"id":"5de13e8e-e098-4ca3-98e7-f13707ead86f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T23:49:59.466005Z","strongest_claim":"Our results demonstrate that the PINN-based periodic solver takes substantially less computational time to achieve almost same accuracy as that obtained by the traditional transient-to-periodic solver.","one_line_summary":"A PINN-based periodic CFD solver is shown to reach nearly the same accuracy as traditional transient-to-periodic methods but with substantially lower computational time for 2D heat diffusion and fluid flow cases.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a neural network optimized solely over one time period using a physics-informed loss can converge to the correct periodic solution without any dependence on or simulation of the transient evolution from initial conditions.","pith_extraction_headline":"Physics-informed neural network computes time-periodic flows by training over one period instead of simulating transients."},"integrity":{"clean":false,"summary":{"advisory":6,"critical":1,"by_detector":{"doi_compliance":{"total":7,"advisory":6,"critical":1,"informational":0}},"informational":0},"endpoint":"/pith/2605.18340/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. 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