An insightful deep learning framework is proposed to solve the well-known Fokker–Planck
(FP) equations that quantify the evolution of the probability density function. It efficiently …

We present a pseudoreversible normalizing flow method for efficiently generating samples
of the state of a stochastic differential equation (SDE) with various initial distributions. The …

In this work, we propose an adaptive learning approach based on temporal normalizing
flows for solving time-dependent Fokker-Planck (TFP) equations. It is well known that …

In this work, we propose a method to learn multivariate probability distributions using sample
path data from stochastic differential equations. Specifically, we consider temporally …

With the rapid development of computational techniques and scientific tools, great progress
of data-driven analysis has been made to extract governing laws of dynamical systems from …

Motivated by the computational difficulties incurred by popular deep learning algorithms for
the generative modeling of temporal densities, we propose a cheap alternative that requires …

In this paper we present a conditional KRnet (cKRnet) based domain decomposed
uncertainty quantification (CKR-DDUQ) approach to propagate uncertainties across different …

Automated Vehicle (AV) validation based on simulated testing requires unbiased evaluation
and high efficiency. One effective solution is to increase the exposure to risky rare events …

Deep learning models have nowadays gained increasing attention in the scientific
computing field due to their inherent nature to capture nonlinear and high-dimensional …

Model discovery aims at autonomously discovering differential equations underlying a
dataset. Approaches based on Physics Informed Neural Networks (PINNs) have shown …