22 citations · 25 across the 5 of their papers we have counts for
8 papers
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
Thomas O'Leary-Roseberry, Raghu Bollapragada
We consider minimizing finite-sum and expectation objective functions via Hessian-averaging based subsampled Newton methods. These methods allow for gradient inexactness and have f…
Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC
Joseph Kirchhoff, Dingcheng Luo, Thomas O'Leary-Roseberry +1
We present a scalable and efficient framework for the inference of spatially-varying parameters of continuum materials from image observations of their deformations. Our goal is th…
A note on the relationship between PDE-based precision operators and Matérn covariances
Umberto Villa, Thomas O'Leary-Roseberry
The purpose of this technical note is to summarize the relationship between the marginal variance and correlation length of a Gaussian random field with Matérn covariance and the c…
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
Lianghao Cao, Thomas O'Leary-Roseberry, Omar Ghattas
We propose an operator learning approach to accelerate geometric Markov chain Monte Carlo (MCMC) for solving infinite-dimensional Bayesian inverse problems (BIPs). While geometric…
Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
Thomas O'Leary-Roseberry, Umberto Villa, Peng Chen +1
Many-query problems, arising from uncertainty quantification, Bayesian inversion, Bayesian optimal experimental design, and optimization under uncertainty-require numerous evaluati…
Ill-Posedness and Optimization Geometry for Nonlinear Neural Network Training
Thomas O'Leary-Roseberry, Omar Ghattas
In this work we analyze the role nonlinear activation functions play at stationary points of dense neural network training problems. We consider a generic least squares loss functi…