Thomas O'Leary-Roseberry

24 papers Journal 24

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	LazyDINO: Fast, Scalable, and Efficiently Amortized Bayesian Inversion via Structure-Exploiting and Surrogate-Driven Measure Transport. J. Mach. Learn. Res. Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef Marzouk, Omar Ghattas
2026	J	`jnl`	Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization. CoRR Xindi Gong, Dingcheng Luo, Thomas O'Leary-Roseberry, Ruanui Nicholson, Omar Ghattas
2025	J	`jnl`	Derivative-Informed Fourier Neural Operator: Universal Approximation and Applications to PDE-Constrained Optimization. CoRR Boyuan Yao, Dingcheng Luo, Lianghao Cao, Nikola B. Kovachki, Thomas O'Leary-Roseberry, Omar Ghattas
2025	J	`jnl`	Derivative-Informed Neural Operator Acceleration of Geometric MCMC for Infinite-Dimensional Bayesian Inverse Problems. J. Mach. Learn. Res. Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas
2025	J	`jnl`	Dimension reduction for derivative-informed operator learning: An analysis of approximation errors. CoRR Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2025	J	`jnl`	Efficient PDE-Constrained Optimization Under High-Dimensional Uncertainty Using Derivative-Informed Neural Operators. SIAM J. Sci. Comput. Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2025	J	`jnl`	Verification and Validation for Trustworthy Scientific Machine Learning. CoRR John D. Jakeman, Lorena A. Barba, Joaquim R. R. A. Martins, Thomas O'Leary-Roseberry
2024	J	`jnl`	A note on the relationship between PDE-based precision operators and Matérn covariances. CoRR Umberto Villa, Thomas O'Leary-Roseberry
2024	J	`jnl`	Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. J. Comput. Phys. Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
2024	J	`jnl`	Efficient geometric Markov chain Monte Carlo for nonlinear Bayesian inversion enabled by derivative-informed neural operators. CoRR Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas
2024	J	`jnl`	Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods. CoRR Thomas O'Leary-Roseberry, Raghu Bollapragada
2024	J	`jnl`	Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC. CoRR Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry, Omar Ghattas
2024	J	`jnl`	LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport. CoRR Lianghao Cao, Joshua Chen, Michael Brennan, Thomas O'Leary-Roseberry, Youssef M. Marzouk, Omar Ghattas
2024	J	`jnl`	SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. J. Open Source Softw. Dingcheng Luo, Peng Chen, Thomas O'Leary-Roseberry, Umberto Villa, Omar Ghattas
2023	J	`jnl`	Efficient PDE-Constrained optimization under high-dimensional uncertainty using derivative-informed neural operators. CoRR Dingcheng Luo, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2023	J	`jnl`	Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. J. Sci. Comput. Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2023	J	`jnl`	Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. J. Comput. Phys. Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas
2022	J	`jnl`	Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning. CoRR Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa, Omar Ghattas
2022	J	`jnl`	Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. CoRR Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen, Omar Ghattas
2022	J	`jnl`	Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems. CoRR Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden, Omar Ghattas
2021	J	`jnl`	Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data. CoRR Thomas O'Leary-Roseberry, Xiaosong Du, Anirban Chaudhuri, Joaquim R. R. A. Martins, Karen Willcox, Omar Ghattas
2020	J	`jnl`	Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. CoRR Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen, Omar Ghattas
2020	J	`jnl`	Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training. CoRR Thomas O'Leary-Roseberry, Omar Ghattas
2020	J	`jnl`	Low Rank Saddle Free Newton: Algorithm and Analysis. CoRR Thomas O'Leary-Roseberry, Nick Alger, Omar Ghattas