Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions. - Peng Chen 024; Keyi Wu; Joshua Chen; Tom O'Leary-Roseberry; Omar Ghattas (NeurIPS, 2019)

Low Rank Saddle Free Newton: Algorithm and Analysis. - Thomas O'Leary-Roseberry; Nick Alger; Omar Ghattas (CoRR, 2020)

Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training. - Thomas O'Leary-Roseberry; Omar Ghattas (CoRR, 2020)

Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs. - Thomas O'Leary-Roseberry; Umberto Villa; Peng Chen 024; Omar Ghattas (CoRR, 2020)

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)

Derivative-informed projected neural network for large-scale Bayesian optimal experimental design. - Keyi Wu; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2022)

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)

Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network. - Keyi Wu; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (J. Sci. Comput., 2023)

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)

A note on the relationship between PDE-based precision operators and Matérn covariances. - Umberto Villa; Thomas O'Leary-Roseberry (CoRR, 2024)

Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods. - Thomas O'Leary-Roseberry; Raghu Bollapragada (CoRR, 2024)

Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC. - Joseph Kirchhoff; Dingcheng Luo; Thomas O'Leary-Roseberry; Omar Ghattas (CoRR, 2024)

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 M. Marzouk; Omar Ghattas (CoRR, 2024)

SOUPy: Stochastic PDE-constrained optimization under high-dimensional uncertainty in Python. - Dingcheng Luo; Peng Chen 024; Thomas O'Leary-Roseberry; Umberto Villa; Omar Ghattas (J. Open Source Softw., 2024)

Derivative-Informed Neural Operator: An efficient framework for high-dimensional parametric derivative learning. - Thomas O'Leary-Roseberry; Peng Chen 024; Umberto Villa; Omar Ghattas (J. Comput. Phys., 2024)

Efficient PDE-Constrained Optimization Under High-Dimensional Uncertainty Using Derivative-Informed Neural Operators. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (SIAM J. Sci. Comput., 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, 2025)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors. - Dingcheng Luo; Thomas O'Leary-Roseberry; Peng Chen 024; Omar Ghattas (CoRR, 2025)