A hard-constrained PINN (H-PINN) achieves lower errors (MAE 0.011-0.023, MRE 2.08-3.14%) than standard PINN when modeling two-domain contaminant transport through GCL/SL liners and supports inverse estimation of degradation half-life.
arXiv preprint arXiv:2602.07031
2 Pith papers cite this work. Polarity classification is still indexing.
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A modified gated PINN with hard-constraint boundary encoding achieves MAE of 0.27-0.43 kPa against FEM references for time-dependent electro-osmotic radial consolidation cases.
citing papers explorer
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Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
A hard-constrained PINN (H-PINN) achieves lower errors (MAE 0.011-0.023, MRE 2.08-3.14%) than standard PINN when modeling two-domain contaminant transport through GCL/SL liners and supports inverse estimation of degradation half-life.
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Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects
A modified gated PINN with hard-constraint boundary encoding achieves MAE of 0.27-0.43 kPa against FEM references for time-dependent electro-osmotic radial consolidation cases.