Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models
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Publication:2138012
DOI10.1016/j.jcp.2022.111202OpenAlexW4223440841WikidataQ115350040 ScholiaQ115350040MaRDI QIDQ2138012
Publication date: 11 May 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2108.12956
Stochastic analysis (60Hxx) Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx)
Related Items (12)
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems ⋮ Learning Invariant Representation of Multiscale Hyperelastic Constitutive Law from Sparse Experimental Data ⋮ Adaptive deep density approximation for fractional Fokker-Planck equations ⋮ Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow ⋮ Fully probabilistic deep models for forward and inverse problems in parametric PDEs ⋮ A Normalizing Field Flow Induced Two-Stage Stochastic Homogenization Method for Random Composite Materials ⋮ MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs Via Monte Carlo Sampling ⋮ PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations ⋮ Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations ⋮ Physics-informed variational inference for uncertainty quantification of stochastic differential equations ⋮ Failure-Informed Adaptive Sampling for PINNs ⋮ Less Emphasis on Hard Regions: Curriculum Learning of PINNs for Singularly Perturbed Convection-Diffusion-Reaction Problems
Uses Software
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