4 papers
Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
Mohammad Nooraiepour, Zezhang Song, Wei Li +1
Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geologi…
Physics-Informed Neural Networks for Predicting Hydrogen Sorption in Geological Formations: Thermodynamically Constrained Deep Learning Integrating Classical Adsorption Theory
Mohammad Nooraiepour, Mohammad Masoudi, Zezhang Song +1
Accurate prediction of hydrogen sorption in fine-grained geological materials is essential for evaluating underground hydrogen storage capacity, assessing caprock integrity, and ch…
Geological CO2 storage assessment in emerging CCS regions: Review of sequestration potential, policy development, and socio-economic factors in Poland
Mohammad Nooraiepour, Karol M. DÄ browski, Mohammad Masoudi +4
Emerging carbon capture and storage (CCS) markets face critical challenges in developing systematic methodologies to assess geological CO2 storage potential under conditions of lim…
Adaptive Physics-Informed Neural Networks with Multi-Category Feature Engineering for Hydrogen Sorption Prediction in Clays, Shales, and Coals
Mohammad Nooraiepour, Mohammad Masoudi, Zezhang Song +1
Accurate prediction of hydrogen sorption in clays, shales, and coals is vital for advancing underground hydrogen storage, natural hydrogen exploration, and radioactive waste contai…