{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Takmili SA"],"funding":["National Research Foundation of Korea"],"pagination":["5401"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12693243"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(23)"],"pubmed_abstract":["Poly(N-isopropylacrylamide) (PNIPAM) hydrogels are temperature-sensitive materials whose swelling is difficult to predict near the gel collapse temperature (GCT). A physics-informed neural network (PINN) was developed in which a stabilized free-energy model is embedded and sparse data for free and uniaxially constrained swelling are assimilated. Across datasets, the PINN reduces test RMSE by 44% at low crosslink density (Nν = 0.0035) and by 65% at higher density (Nν = 0.02), with coverage inside ±RMSE bands improving from 61.9% to 76.2% for free swelling at Nν = 0.0035. Under constraint, test relative error decreases from 19.95% to 11.86% (n = 1) and from 9.19% to 5.90% (n = 3), while preserving thermodynamic stability. The method sharpens the transition slope near the gel-collapse tempera"],"journal":["Materials (Basel, Switzerland)"],"pubmed_title":["Physics-Informed Neural Networks for Thermo-Responsive Hydrogel Swelling: Integrating Constitutive Models with Sparse Experimental Data."],"pmcid":["PMC12693243"],"funding_grant_id":["Project No. RS-2024-00341914"],"pubmed_authors":["Ostadrahimi A","Baghani M","Choi E","Takmili SA"],"additional_accession":[]},"is_claimable":false,"name":"Physics-Informed Neural Networks for Thermo-Responsive Hydrogel Swelling: Integrating Constitutive Models with Sparse Experimental Data.","description":"Poly(N-isopropylacrylamide) (PNIPAM) hydrogels are temperature-sensitive materials whose swelling is difficult to predict near the gel collapse temperature (GCT). A physics-informed neural network (PINN) was developed in which a stabilized free-energy model is embedded and sparse data for free and uniaxially constrained swelling are assimilated. Across datasets, the PINN reduces test RMSE by 44% at low crosslink density (Nν = 0.0035) and by 65% at higher density (Nν = 0.02), with coverage inside ±RMSE bands improving from 61.9% to 76.2% for free swelling at Nν = 0.0035. Under constraint, test relative error decreases from 19.95% to 11.86% (n = 1) and from 9.19% to 5.90% (n = 3), while preserving thermodynamic stability. The method sharpens the transition slope near the gel-collapse tempera","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-05-26T17:04:38.936Z","creation":"2026-05-24T03:11:15.201Z"},"accession":"S-EPMC12693243","cross_references":{"pubmed":["41374242"],"doi":["10.3390/ma18235401"]}}