Learning physics-based models from data: perspectives from inverse problems and model reduction
This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …
inverse problems and model reduction. These fields develop formulations that integrate data …
A fast and scalable computational framework for large-scale high-dimensional Bayesian optimal experimental design
We develop a fast and scalable computational framework to solve Bayesian optimal
experimental design problems governed by partial differential equations (PDEs) with …
experimental design problems governed by partial differential equations (PDEs) with …
hIPPYlib-MUQ: A Bayesian inference software framework for integration of data with complex predictive models under uncertainty
Bayesian inference provides a systematic framework for integration of data with
mathematical models to quantify the uncertainty in the solution of the inverse problem …
mathematical models to quantify the uncertainty in the solution of the inverse problem …
Sparse recovery of elliptic solvers from matrix-vector products
In this work, we show that solvers of elliptic boundary value problems in dimensions can be
approximated to accuracy from only matrix-vector products with carefully chosen vectors …
approximated to accuracy from only matrix-vector products with carefully chosen vectors …
An offline-online decomposition method for efficient linear Bayesian goal-oriented optimal experimental design: Application to optimal sensor placement
Bayesian optimal experimental design (OED) plays an important role in minimizing model
uncertainty with limited experimental data in a Bayesian framework. In many applications …
uncertainty with limited experimental data in a Bayesian framework. In many applications …
KSPHPDDM and PCHPDDM: Extending PETSc with advanced Krylov methods and robust multilevel overlapping Schwarz preconditioners
Contemporary applications in computational science and engineering often require the
solution of linear systems which may be of different sizes, shapes, and structures. The goal …
solution of linear systems which may be of different sizes, shapes, and structures. The goal …
Hierarchical off-diagonal low-rank approximation of Hessians in inverse problems, with application to ice sheet model initialization
Obtaining lightweight and accurate approximations of discretized objective functional
Hessians in inverse problems governed by partial differential equations (PDEs) is essential …
Hessians in inverse problems governed by partial differential equations (PDEs) is essential …
Point Spread Function Approximation of High-Rank Hessians with Locally Supported Nonnegative Integral Kernels
We present an efficient matrix-free point spread function (PSF) method for approximating
operators that have locally supported nonnegative integral kernels. The PSF-based method …
operators that have locally supported nonnegative integral kernels. The PSF-based method …
An efficient method for goal-oriented linear bayesian optimal experimental design: Application to optimal sensor placemen
Optimal experimental design (OED) plays an important role in the problem of identifying
uncertainty with limited experimental data. In many applications, we seek to minimize the …
uncertainty with limited experimental data. In many applications, we seek to minimize the …
Scalable Physics-Based Maximum Likelihood Estimation Using Hierarchical Matrices
Y Chen, M Anitescu - SIAM/ASA Journal on Uncertainty Quantification, 2023 - SIAM
Physics-based covariance models provide a systematic way to construct covariance models
that are consistent with the underlying physical laws in Gaussian process analysis. The …
that are consistent with the underlying physical laws in Gaussian process analysis. The …