معرفی
Gege Wen is an Assistant Professor at Imperial College London with co-appointments in Earth Science Engineering and I-X (Imperial + AI). Their research focuses on computational methods and AI for Energy Transition, particularly subsurface energy storage and CO2 geological storage.
- Ph.D. in Energy Sciences & Engineering, Stanford Doerr School of Sustainability
- M.S. in Fluid Mechanics and Hydrology, Stanford University
- B.S. in Mineral Engineering (Honors), University of Toronto
Their work combines deep learning models like convolutional neural networks (Wen et al., 2021) and enhanced Fourier neural operators (Wen et al., 2022) to predict CO2 plume migration and pressure dynamics in heterogeneous and anisotropic permeability maps. Recent projects include CCSNet.ai, a web application providing 1,000+ daily predictions for subsurface CO2 storage scenarios.
Current research emphasizes AI-driven solutions for energy transition challenges, including synthetic data generation, multiphase flow modeling, and deployment of pre-trained models for isotropic/anisotropic cases. The group actively seeks PhD students interested in AI-fluid flow intersections.
