Thomas O'Leary-Roseberry
Orcid: 0000-0002-8938-7074
According to our database1,
Thomas O'Leary-Roseberry
authored at least 15 papers
between 2019 and 2024.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
On csauthors.net:
Bibliography
2024
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning.
J. Comput. Phys., January, 2024
SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python.
J. Open Source Softw., 2024
Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC.
CoRR, 2024
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods.
CoRR, 2024
A note on the relationship between PDE-based precision operators and Matérn covariances.
CoRR, 2024
Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators.
CoRR, 2024
2023
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network.
J. Sci. Comput., April, 2023
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems.
J. Comput. Phys., 2023
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators.
CoRR, 2023
2022
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design.
CoRR, 2022
2021
CoRR, 2021
2020
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs.
CoRR, 2020
CoRR, 2020
2019
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019