Zobrazeno 1 - 10
of 398
pro vyhledávání: '"Ghattas, Omar"'
This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynam
Externí odkaz:
http://arxiv.org/abs/2408.15286
This work considers the computation of risk measures for quantities of interest governed by PDEs with Gaussian random field parameters using Taylor approximations. While efficient, Taylor approximations are local to the point of expansion, and hence
Externí odkaz:
http://arxiv.org/abs/2408.06615
This paper establishes optimal convergence rates for estimation of structured covariance operators of Gaussian processes. We study banded operators with kernels that decay rapidly off-the-diagonal and $L^q$-sparse operators with an unordered sparsity
Externí odkaz:
http://arxiv.org/abs/2408.02109
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 the nondestructive identification of arbitrary damage, defects, anomalie
Externí odkaz:
http://arxiv.org/abs/2408.10217
We present an efficient and scalable algorithm for performing matrix-vector multiplications ("matvecs") for block Toeplitz matrices. Such matrices, which are shift-invariant with respect to their blocks, arise in the context of solving inverse proble
Externí odkaz:
http://arxiv.org/abs/2407.13066
Estimating the parameters of compact binaries which coalesce and produce gravitational waves is a challenging Bayesian inverse problem. Gravitational-wave parameter estimation lies within the class of multifidelity problems, where a variety of models
Externí odkaz:
http://arxiv.org/abs/2405.19407
Autor:
Al-Ghattas, Omar, Sanz-Alonso, Daniel
This paper studies sparse covariance operator estimation for nonstationary Gaussian processes with sharply varying marginal variance and small correlation lengthscale. We introduce a covariance operator estimator that adaptively thresholds the sample
Externí odkaz:
http://arxiv.org/abs/2405.18562
Autor:
Sanz-Alonso, Daniel, Al-Ghattas, Omar
This is a concise mathematical introduction to Monte Carlo methods, a rich family of algorithms with far-reaching applications in science and engineering. Monte Carlo methods are an exciting subject for mathematical statisticians and computational an
Externí odkaz:
http://arxiv.org/abs/2405.16359
We propose an operator learning approach to accelerate geometric Markov chain Monte Carlo (MCMC) for solving infinite-dimensional Bayesian inverse problems (BIPs). While geometric MCMC employs high-quality proposals that adapt to posterior local geom
Externí odkaz:
http://arxiv.org/abs/2403.08220
This paper investigates covariance operator estimation via thresholding. For Gaussian random fields with approximately sparse covariance operators, we establish non-asymptotic bounds on the estimation error in terms of the sparsity level of the covar
Externí odkaz:
http://arxiv.org/abs/2310.16933