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ORCIDThomas O'Leary-Roseberry
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2020 – today
- 2024
[j4]Thomas O'Leary-Roseberry
, Peng Chen, Umberto Villa, Omar Ghattas:
Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. J. Comput. Phys. 496: 112555 (2024)- [j3]Dingcheng Luo, Peng Chen, Thomas O'Leary-Roseberry, Umberto Villa, Omar Ghattas:
SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. J. Open Source Softw. 9(99): 6101 (2024) - [i12]Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas:
Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators. CoRR abs/2403.08220 (2024) - [i11]Umberto Villa, Thomas O'Leary-Roseberry:
A note on the relationship between PDE-based precision operators and Matérn covariances. CoRR abs/2407.00471 (2024) - [i10]Thomas O'Leary-Roseberry, Raghu Bollapragada:
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods. CoRR abs/2408.07268 (2024) - [i9]Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry, Omar Ghattas:
Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC. CoRR abs/2408.10217 (2024) - 2023
- [j2]Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas:
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. J. Comput. Phys. 486: 112104 (2023) - [j1]Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas:
Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. J. Sci. Comput. 95(1): 30 (2023) - [i8]Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas:
Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. CoRR abs/2305.20053 (2023) - 2022
- [i7]Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas:
Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. CoRR abs/2201.07925 (2022) - [i6]Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas:
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. CoRR abs/2206.10745 (2022) - [i5]Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas:
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. CoRR abs/2210.03008 (2022) - 2021
- [i4]Thomas O'Leary-Roseberry, Xiaosong Du, Anirban Chaudhuri, Joaquim R. R. A. Martins, Karen Willcox, Omar Ghattas:
Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data. CoRR abs/2112.07096 (2021) - 2020
- [i3]Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas:
Low Rank Saddle Free Newton: Algorithm and Analysis. CoRR abs/2002.02881 (2020) - [i2]Thomas O'Leary-Roseberry, Omar Ghattas:
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training. CoRR abs/2002.02882 (2020) - [i1]Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas:
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. CoRR abs/2011.15110 (2020)
2010 – 2019
- 2019
- [c1]Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas:
Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. NeurIPS 2019: 15104-15113
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