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A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty
In this paper, we propose a local squared Wasserstein-2 (W_2) method to solve the inverse problem of reconstructing models with uncertain latent variables or parameters.
An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
We analyze the Wasserstein distance ($W$-distance) between two probability distributions associated with two multidimensional jump-diffusion processes.
Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations
We provide an analysis of the squared Wasserstein-2 ($W_2$) distance between two probability distributions associated with two stochastic differential equations (SDEs).
A Spectral Approach for Learning Spatiotemporal Neural Differential Equations
Rapidly developing machine learning methods has stimulated research interest in computationally reconstructing differential equations (DEs) from observational data which may provide additional insight into underlying causative mechanisms.
