Solving inverse problems using conditional invertible neural networks
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Publication:2120777
DOI10.1016/j.jcp.2021.110194OpenAlexW3046462119MaRDI QIDQ2120777
Nicholas Zabaras, Govinda Anantha Padmanabha
Publication date: 1 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110194
multiphase flowhigh-dimensionalconditional invertible neural networkflow-based generative modelinverse surrogate modeling
Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx) Geophysics (86Axx)
Related Items (6)
Normalizing field flows: solving forward and inverse stochastic differential equations using physics-informed flow models ⋮ VI-DGP: a variational inference method with deep generative prior for solving high-dimensional inverse problems ⋮ A Normalizing Field Flow Induced Two-Stage Stochastic Homogenization Method for Random Composite Materials ⋮ A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows ⋮ Bi-fidelity variational auto-encoder for uncertainty quantification ⋮ Variable-order approach to nonlocal elasticity: theoretical formulation, order identification via deep learning, and applications
Uses Software
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