A Spectral Approach for Learning Spatiotemporal Neural Differential Equations. - Mingtao Xia; Xiangting Li 001; Qijing Shen; Tom Chou (CoRR, 2023)

Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations. - Mingtao Xia; Xiangting Li 001; Qijing Shen; Tom Chou (CoRR, 2024)

An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks. - Mingtao Xia; Xiangting Li 001; Qijing Shen; Tom Chou (CoRR, 2024)

A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty. - Mingtao Xia; Qijing Shen (CoRR, 2024)

Learning unbounded-domain spatiotemporal differential equations using adaptive spectral methods. - Mingtao Xia; Xiangting Li 001; Qijing Shen; Tom Chou (J. Appl. Math. Comput., 2024)

A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems. - Mingtao Xia; Qijing Shen; Philip K. Maini; Eamonn A. Gaffney; Alex Mogilner (CoRR, 2025)

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks. - Mingtao Xia; Qijing Shen (CoRR, 2025)