Thomas O'Leary-Roseberry

According to our database, Thomas O'Leary-Roseberry authored at least 22 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Performance of Neural and Polynomial Operator Surrogates.

Josephine Westermann, Benno Huber, Thomas O'Leary-Roseberry, Jakob Zech

CoRR, April, 2026

Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization.

Xindi Gong, Dingcheng Luo, Thomas O'Leary-Roseberry, Ruanui Nicholson, Omar Ghattas

CoRR, March, 2026

LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport.

Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, Omar Ghattas

J. Mach. Learn. Res., 2026

2025

Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization.

Boyuan Yao, Dingcheng Luo, Lianghao Cao, Nikola B. Kovachki, Thomas O'Leary-Roseberry, Omar Ghattas

CoRR, December, 2025

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors.

Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas

CoRR, April, 2025

Verification and Validation for Trustworthy Scientific Machine Learning.

John D. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins, Thomas O'Leary-Roseberry

CoRR, February, 2025

Efficient PDE-Constrained Optimization Under High-Dimensional Uncertainty Using Derivative-Informed Neural Operators.

Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas

SIAM J. Sci. Comput., 2025

Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems.

Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas

J. Mach. Learn. Res., 2025

2024

Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning.

Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas

J. Comput. Phys., January, 2024

SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python.

Dingcheng Luo, Peng Chen, Thomas O'Leary-Roseberry, Umberto Villa, Omar Ghattas

J. Open Source Softw., 2024

Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC.

Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry, Omar Ghattas

CoRR, 2024

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods.

Thomas O'Leary-Roseberry, Raghu Bollapragada

CoRR, 2024

A note on the relationship between PDE-based precision operators and Matérn covariances.

Umberto Villa, Thomas O'Leary-Roseberry

CoRR, 2024

Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators.

Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas

CoRR, 2024

2023

Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network.

Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas

J. Sci. Comput., April, 2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems.

Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas

J. Comput. Phys., 2023

2022

Derivative-informed projected neural network for large-scale Bayesian optimal experimental design.

Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas

CoRR, 2022

2021

Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data.

Thomas O'Leary-Roseberry, Xiaosong Du, Anirban Chaudhuri, Joaquim R. R. A. Martins, Karen Willcox, Omar Ghattas

CoRR, 2021

2020

Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs.

Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas

CoRR, 2020

Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training.

Thomas O'Leary-Roseberry, Omar Ghattas

CoRR, 2020

Low Rank Saddle Free Newton: Algorithm and Analysis.

Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas

CoRR, 2020

2019

Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions.

Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019