Thomas O'Leary-Roseberry

According to our database, Thomas O'Leary-Roseberry authored at least 11 papers between 2019 and 2024.

Collaborative distances:

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

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

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

Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators.

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

CoRR, 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