Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks
From MaRDI portal
Publication:2683056
DOI10.1016/j.jcp.2022.111841OpenAlexW4311761049MaRDI QIDQ2683056
Liang Yan, Yan-yan Wang, Yong-Chao Li
Publication date: 3 February 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111841
Parametric inference (62Fxx) Artificial intelligence (68Txx) Probabilistic methods, stochastic differential equations (65Cxx)
Related Items (3)
Adaptive Ensemble Kalman Inversion with Statistical Linearization ⋮ Reduced-order model-based variational inference with normalizing flows for Bayesian elliptic inverse problems ⋮ Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems
Uses Software
Cites Work
- Stochastic spectral methods for efficient Bayesian solution of inverse problems
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
- The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
- Statistical and computational inverse problems.
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- Transfer learning based multi-fidelity physics informed deep neural network
- When and why PINNs fail to train: a neural tangent kernel perspective
- Physics-informed neural networks for high-speed flows
- Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
- Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems
- A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
- Stein variational gradient descent with local approximations
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- A deep learning energy method for hyperelasticity and viscoelasticity
- Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems
- Inverse problems: A Bayesian perspective
- Posterior consistency for Gaussian process approximations of Bayesian posterior distributions
- Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ
- Inverse Problem Theory and Methods for Model Parameter Estimation
- Computational Methods for Inverse Problems
- Solving high-dimensional partial differential equations using deep learning
- AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS
- An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks
- Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler
- DeepXDE: A Deep Learning Library for Solving Differential Equations
- An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems
- Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks
- Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks
- fPINNs: Fractional Physics-Informed Neural Networks
This page was built for publication: Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks
