Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning

21 June 2022

Thomas O'Leary-Roseberry

Papers citing "Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning"

21 / 21 papers shown

Title
Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems? Evan Scope Crafts Umberto Villa 44 0 0 04 Mar 2025
Verification and Validation for Trustworthy Scientific Machine Learning John D. Jakeman Lorena A. Barba J. Martins Thomas O'Leary-Roseberry AI4CE 56 0 0 21 Feb 2025
Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis Qiao Chen Elise Arnaud Ricardo Baptista O. Zahm 40 1 0 19 Jun 2024
BiLO: Bilevel Local Operator Learning for PDE inverse problems Ray Zirui Zhang Xiaohui Xie John S. Lowengrub 68 1 0 27 Apr 2024
Neural Operator induced Gaussian Process framework for probabilistic solution of parametric partial differential equations Sawan Kumar R. Nayek Souvik Chakraborty 34 2 0 24 Apr 2024
Geometric Neural Operators (GNPs) for Data-Driven Deep Learning of Non-Euclidean Operators Blaine Quackenbush P. Atzberger AI4CE 27 0 0 16 Apr 2024
PETScML: Second-order solvers for training regression problems in Scientific Machine Learning Stefano Zampini Umberto Zerbinati George Turkyyiah David E. Keyes 35 4 0 18 Mar 2024
Multifidelity linear regression for scientific machine learning from scarce data Elizabeth Qian Dayoung Kang Vignesh Sella Anirban Chaudhuri AI4CE 81 1 0 13 Mar 2024
A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks Graham Pash M. Hassanaly S. Yellapantula AI4CE 43 0 0 28 Feb 2024
Operator Learning: Algorithms and Analysis Nikola B. Kovachki S. Lanthaler Andrew M. Stuart 40 22 0 24 Feb 2024
A Mathematical Guide to Operator Learning Nicolas Boullé Alex Townsend 31 36 0 22 Dec 2023
A unified framework for learning with nonlinear model classes from arbitrary linear samples Ben Adcock Juan M. Cardenas N. Dexter 31 3 0 25 Nov 2023
Operator Learning for Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods Chuanqi Chen Jin-Long Wu AI4CE 26 0 0 20 Nov 2023
Deep Operator Network Approximation Rates for Lipschitz Operators Ch. Schwab A. Stein Jakob Zech 30 9 0 19 Jul 2023
Solving multiphysics-based inverse problems with learned surrogates and constraints Ziyi Yin Rafael Orozco M. Louboutin Felix J. Herrmann AI4CE 69 9 0 18 Jul 2023
CS4ML: A general framework for active learning with arbitrary data based on Christoffel functions Ben Adcock Juan M. Cardenas N. Dexter 24 6 0 01 Jun 2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators Dingcheng Luo Thomas O'Leary-Roseberry Peng Chen Omar Ghattas AI4CE 21 15 0 31 May 2023
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems Lianghao Cao Thomas O'Leary-Roseberry Prashant K. Jha J. Oden Omar Ghattas 23 26 0 06 Oct 2022
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks Thomas O'Leary-Roseberry Xiaosong Du A. Chaudhuri J. Martins Karen E. Willcox Omar Ghattas 30 22 0 14 Dec 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 205 2,282 0 18 Oct 2020
Projected Stein Variational Gradient Descent Peng Chen Omar Ghattas BDL 55 68 0 09 Feb 2020