About Zhao Beichen Zhao Beichen Visiting Student, Applied Mathematics and Computational Science Physics-informed Neural Networks Scientific Machine Learning Subsurface Flow Beichen Zhao is a Ph.D. candidate at China University of Petroleum–Beijing and a visiting student in the Applied Mathematics and Computational Science program at KAUST. His research focuses on physics-informed neural networks and surrogate modeling for subsurface flow, with applications in CO₂ enhanced oil recovery, geological carbon storage, and geothermal reservoir simulation. Events Presented Events Sep 13 - Sep 19, 2026 Physics-Informed Spatiotemporal Surrogate Modeling and Robust Operational Optimization for CO₂ Applications in Subsurface Flow under Permeability Uncertainty Zhao Beichen, Visiting Student, Applied Mathematics and Computational Science Sep 17, 12:00 - 13:00 B9 R2325 Physics-informed Neural Networks Deep learning numerical simulations uncertainty quantification subsurface fluid flow This talk presents a physics-informed spatiotemporal surrogate that predicts coupled CO₂ storage and geothermal responses under geological uncertainty and enables rapid, risk-aware optimization of staged well controls.
Physics-Informed Spatiotemporal Surrogate Modeling and Robust Operational Optimization for CO₂ Applications in Subsurface Flow under Permeability Uncertainty Zhao Beichen, Visiting Student, Applied Mathematics and Computational Science Sep 17, 12:00 - 13:00 B9 R2325 Physics-informed Neural Networks Deep learning numerical simulations uncertainty quantification subsurface fluid flow This talk presents a physics-informed spatiotemporal surrogate that predicts coupled CO₂ storage and geothermal responses under geological uncertainty and enables rapid, risk-aware optimization of staged well controls.
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