Guang-Hua Ji

Data-Driven Free-Energy Learning from Trajectories via Structure-Preserving Exponential Time Differencing

Structure-preserving numerical methods for gradient flows have been extensively developed when the governing equations and free energies are known. However, accurately predicting dynamics from observational data while preserving intrinsic physical properties remains challenging. Although neural operators, such as Fouri...