A PINN transfer learning framework for coal methane sorption reaches R²=0.932 on held-out data with 227% improvement over classical isotherms and identifies Monte Carlo Dropout as the best uncertainty method while ensembles degrade under shared physics constraints.
Molecular simulation on H2 adsorption in nanopores and effects of cushion gas: implications for underground hydrogen storageinshalereservoirs
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper reviews recent developments and unresolved challenges in cardiac mechanics modeling, arguing that identifying essential complexities versus safe simplifications is key to clinical translation.
citing papers explorer
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Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
A PINN transfer learning framework for coal methane sorption reaches R²=0.932 on held-out data with 227% improvement over classical isotherms and identifies Monte Carlo Dropout as the best uncertainty method while ensembles degrade under shared physics constraints.
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Cardiac mechanics modeling: recent developments and current challenges
The paper reviews recent developments and unresolved challenges in cardiac mechanics modeling, arguing that identifying essential complexities versus safe simplifications is key to clinical translation.