Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations (Q6154967)

scientific article; zbMATH DE number 7805930

Language	Label	Description	Also known as
English	Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations	scientific article; zbMATH DE number 7805930

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instance of

scholarly article

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title

Bayesian Deep Learning Framework for Uncertainty Quantification in Stochastic Partial Differential Equations (English)

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SIAM Journal on Scientific Computing

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publication date

16 February 2024

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Mathematics Subject Classification ID

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Bayesian neural network

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Hamiltonian Monte Carlo

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stochastic partial differential equation

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uncertainty quantification

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MaRDI profile type

MaRDI publication profile

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full work available at URL

https://doi.org/10.1137/23m1560574

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On the equivalence of dynamically orthogonal and bi-orthogonal methods: theory and numerical simulations

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Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods

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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

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Simulator-free solution of high-dimensional stochastic elliptic partial differential equations using deep neural networks

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Q5349052

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Automated solution of differential equations by the finite element method. The FEniCS book

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Numerical Optimization

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Feature representation and discrimination based on Gaussian mixture model probability densities -- practices and algorithms

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Q5396710

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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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Physics-informed variational inference for uncertainty quantification of stochastic differential equations

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

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On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks

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Q5268569

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Q3583848

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The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations

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

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Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations

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Adversarial uncertainty quantification in physics-informed neural networks

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Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity

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

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Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems

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PI-VAE: physics-informed variational auto-encoder for stochastic differential equations

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

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Wikidata QID

Q129136843

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Sitelinks

Mathematics(1 entry)

mardi Publication:6154967