420 Delaware St. SE
Minneapolis, MN 55455
United States
University of Minnesota - Twin Cities
Phisics-informed neural networks, inverse problems, backward advection-dispersion equations, deep neural network training, importance sampling, parabolic equations
redox flow battery, machine learning, energy storage, physics-constrained neural networks, electrochemical model
PINN method, parabolic PDEs, inverse PDEs, backward ADEs, DNN approximation
Differentiable programming; Meshfree methods; Hybrid approximation; Physics-informed learning; Variational formulation; Surrogate model; Artificial intelligence
