3 papers
cs.LG2025
Efficient reconstruction of multidimensional random field models with heterogeneous data using stochastic neural networks
Mingtao Xia, Qijing Shen
In this paper, we analyze the scalability of a recent Wasserstein-distance approach for training stochastic neural networks (SNNs) to reconstruct multidimensional random field mode…
cs.LG2025
A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks
Mingtao Xia, Qijing Shen
In this work, we propose a novel generalized Wasserstein-2 distance approach for efficiently training stochastic neural networks to reconstruct random field models, where the targe…
cs.LG2025
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 Maini +2
In this work, we propose and analyze a new local time-decoupled squared Wasserstein-2 method for reconstructing the distribution of unknown parameters in dynamical systems. Specifi…