Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (Q2683056)

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English	Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks	scientific article

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instance of

scholarly article

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title

Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks (English)

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Journal of Computational Physics

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publication date

3 February 2023

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zbMATH Keywords

physics-informed neural networks

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Bayesian inverse problem

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surrogate modeling

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describes a project that uses

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MaRDI publication profile

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full work available at URL

https://doi.org/10.1016/j.jcp.2022.111841

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cites work

Inverse Problem Theory and Methods for Model Parameter Estimation

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Computational Methods for Inverse Problems

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Statistical and computational inverse problems.

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Inverse problems: A Bayesian perspective

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An Adaptive Surrogate Modeling Based on Deep Neural Networks for Large-Scale Bayesian Inverse Problems

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Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems

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AN ADAPTIVE MULTIFIDELITY PC-BASED ENSEMBLE KALMAN INVERSION FOR INVERSE PROBLEMS

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Stochastic spectral methods for efficient Bayesian solution of inverse problems

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Adaptive Construction of Surrogates for the Bayesian Solution of Inverse Problems

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Posterior consistency for Gaussian process approximations of Bayesian posterior distributions

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Solving high-dimensional partial differential equations using deep learning

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems

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A deep learning energy method for hyperelasticity and viscoelasticity

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An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications

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Learning in Modal Space: Solving Time-Dependent Stochastic PDEs Using Physics-Informed Neural Networks

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Physics-informed neural networks for high-speed flows

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fPINNs: Fractional Physics-Informed Neural Networks

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Transfer learning based multi-fidelity physics informed deep neural network

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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An Acceleration Strategy for Randomize-Then-Optimize Sampling Via Deep Neural Networks

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When and why PINNs fail to train: a neural tangent kernel perspective

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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Solving Allen-Cahn and Cahn-Hilliard Equations using the Adaptive Physics Informed Neural Networks

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A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems

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Stein variational gradient descent with local approximations

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Interacting Langevin Diffusions: Gradient Structure and Ensemble Kalman Sampler

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Identifiers

DOI

10.1016/j.jcp.2022.111841

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Mathematics Subject Classification ID

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Mathematics(1 entry)

mardi Publication:2683056