1 citations · 1 across the 4 of their papers we have counts for
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…
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…
Geological CO2 storage assessment in emerging CCS regions: Review of sequestration potential, policy development, and socio-economic factors in Poland
Mohammad Nooraiepour, Karol M. Dąbrowski, 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…