171 citations · 898 across the 46 of their papers we have counts for
7 papers · 1 filter
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)…
Optimal design of chemoepitaxial guideposts for directed self-assembly of block copolymer systems using an inexact-Newton algorithm
Dingcheng Luo, Lianghao Cao, Peng Chen +2
Directed self-assembly (DSA) of block-copolymers (BCPs) is one of the most promising developments in the cost-effective production of nanoscale devices. The process makes use of th…
Interior over-penalized enriched Galerkin methods for second order elliptic equations
Jeonghun J. Lee, Omar Ghattas
In this paper we propose a variant of enriched Galerkin methods for second order elliptic equations with over-penalization of interior jump terms. The bilinear form with interior o…
Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport
Ricardo Baptista, Lianghao Cao, Joshua Chen +4
We consider the Bayesian calibration of models describing the phenomenon of block copolymer (BCP) self-assembly using image data produced by microscopy or X-ray scattering techniqu…
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…
A stochastic Stein Variational Newton method
Alex Leviyev, Joshua Chen, Yifei Wang +2
Stein variational gradient descent (SVGD) is a general-purpose optimization-based sampling algorithm that has recently exploded in popularity, but is limited by two issues: it is k…
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…