Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks

DOI10.1016/j.jcp.2022.111270OpenAlexW3198736066MaRDI QIDQ2671386

Yihang Gao, Michael Kwok-Po Ng

Publication date: 3 June 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.13054

zbMATH Keywords

machine learningdata-driven scientific computinggeneralization theorysimi-supervised learninguncertainty quantificationsWasserstein generative adversarial networks

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (4)

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems ⋮ Neural networks based on power method and inverse power method for solving linear eigenvalue problems ⋮ Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons ⋮ FDM-PINN: Physics-informed neural network based on fictitious domain method

Uses Software

• L-BFGS
• DiffSharp
• ImageNet
• Adam
• POT
• AlexNet
• DGM
• FPINNs
• Wasserstein GAN
• DeepONet
• PINNsNTK

Cites Work

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