36 citations · 155 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems
Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha +2
We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs)…
Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning
Thomas O'Leary-Roseberry, Peng Chen, Umberto Villa +1
We propose derivative-informed neural operators (DINOs), a general family of neural networks to approximate operators as infinite-dimensional mappings from input function spaces to…
Large-scale Bayesian optimal experimental design with derivative-informed projected neural network
Keyi Wu, Thomas O'Leary-Roseberry, Peng Chen +1
We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with infinite-dimensional parameter fie…
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…