Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (Q6202135)

scientific article; zbMATH DE number 7807978

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English	Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning	scientific article; zbMATH DE number 7807978

Statements

instance of

scholarly article

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title

Derivative-informed neural operator: an efficient framework for high-dimensional parametric derivative learning (English)

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author

Thomas O'Leary-Roseberry

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Journal of Computational Physics

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publication date

21 February 2024

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full work available at URL

https://arxiv.org/abs/2206.10745

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zbMATH Keywords

derivative learning

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high-dimensional Jacobians

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neural operators

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parametric surrogates

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parametrized PDEs

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derivative-informed dimension reduction

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MaRDI profile type

MaRDI publication profile

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cites work

Model reduction and neural networks for parametric PDEs

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POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decomposition

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A Stochastic Newton MCMC Method for Large-Scale Statistical Inverse Problems with Application to Seismic Inversion

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Tensor Train Construction From Tensor Actions, With Application to Compression of Large High Order Derivative Tensors

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Analysis of the Hessian for inverse scattering problems. III: Inverse medium scattering of electromagnetic waves in three dimensions.

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A scalable algorithm for MAP estimators in Bayesian inverse problems with Besov priors

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A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems Part I: The Linearized Case, with Application to Global Seismic Inversion

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Hessian-based adaptive sparse quadrature for infinite-dimensional Bayesian inverse problems

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Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian Approximations

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Scalable and efficient algorithms for the propagation of uncertainty from data through inference to prediction for large-scale problems, with application to flow of the antarctic ice sheet

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A Computational Framework for Infinite-Dimensional Bayesian Inverse Problems, Part II: Stochastic Newton MCMC with Application to Ice Sheet Flow Inverse Problems

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Optimal design of acoustic metamaterial cloaks under uncertainty

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A-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems with Regularized $\ell_0$-Sparsification

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A Fast and Scalable Method for A-Optimal Design of Experiments for Infinite-dimensional Bayesian Nonlinear Inverse Problems

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A-optimal encoding weights for nonlinear inverse problems, with application to the Helmholtz inverse problem

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A Fast and Scalable Computational Framework for Large-Scale High-Dimensional Bayesian Optimal Experimental Design

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An Offline-Online Decomposition Method for Efficient Linear Bayesian Goal-Oriented Optimal Experimental Design: Application to Optimal Sensor Placement

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Large-scale Bayesian optimal experimental design with derivative-informed projected neural network

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

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Q4558473

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An Analysis of Infinite Dimensional Bayesian Inverse Shape Acoustic Scattering and Its Numerical Approximation

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Gradient-Based Dimension Reduction of Multivariate Vector-Valued Functions

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Identifiers

arXiv ID

2206.10745

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DOI

10.1016/j.jcp.2023.112555

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Mathematics Subject Classification ID

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Sitelinks

Mathematics(1 entry)

mardi Publication:6202135