Cover Image

Accelerating seismic inversion and uncertainty quantification with efficient high-rank Hessian approximations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hu, Mathew, Alger, Nick, Nammour, Rami, Ghattas, Omar
Format:	Preprint
Published:	2025
Subjects:	Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Point spread function approximation of high rank Hessians with locally supported non-negative integral kernels
by: Alger, Nick, et al.
Published: (2023)

Tucker Tensor Train Taylor Series
by: Alger, Nick, et al.
Published: (2026)

Real-time aerodynamic load estimation for hypersonics via strain-based inverse maps
by: Pham, Julie, et al.
Published: (2024)

Fast And Scalable FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices With Application to Linear Inverse Problems Governed by Autonomous Dynamical Systems
by: Venkat, Sreeram, et al.
Published: (2024)

Dynamical low-rank approximation for the semiclassical Schrodinger equation with uncertainties
by: Liu, Liu, et al.
Published: (2026)

A polarization tensor approximation for the Hessian in iterative solvers for non-linear inverse problems
by: Watson, F. M., et al.
Published: (2021)

Goal-Oriented Real-Time Bayesian Inference for Linear Autonomous Dynamical Systems With Application to Digital Twins for Tsunami Early Warning
by: Henneking, Stefan, et al.
Published: (2025)

A two-step surrogate method for sequential uncertainty quantification in high-dimensional inverse problems
by: Yang, Ningxin, et al.
Published: (2024)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
by: Luo, Dingcheng, et al.
Published: (2025)

Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
by: Cao, Lianghao, et al.
Published: (2024)

LazyDINO: Fast, scalable, and efficiently amortized Bayesian inversion via structure-exploiting and surrogate-driven measure transport
by: Cao, Lianghao, et al.
Published: (2024)

On uncertainty quantification of eigenvalues and eigenspaces with higher multiplicity
by: Dölz, Jürgen, et al.
Published: (2022)

A conditional normalizing flow for domain decomposed uncertainty quantification
by: Li, Sen, et al.
Published: (2024)

Accelerated decomposition of bistochastic kernel matrices by low rank approximation
by: Vales, Chris, et al.
Published: (2025)

A First Course in Monte Carlo Methods
by: Sanz-Alonso, Daniel, et al.
Published: (2024)

Latent representation learning based model correction and uncertainty quantification for PDEs
by: Zhou, Wenwen, et al.
Published: (2026)

Low-rank approximation of analytic kernels
by: Webb, Marcus
Published: (2025)

Efficient forward and inverse uncertainty quantification for dynamical systems based on dimension reduction and Kriging surrogate modeling in functional space
by: Song, Zhouzhou, et al.
Published: (2025)

Deep set based operator learning with uncertainty quantification
by: Ma, Lei, et al.
Published: (2025)

A fast low-rank inversion algorithm of dielectric matrix in GW approximation
by: Zhou, Zhengbang, et al.
Published: (2024)

Low-rank and sparse approximations for contact mechanics
by: Kollepara, Kiran Sagar
Published: (2024)

Data-driven dimensionally decomposed generalized polynomial chaos expansion for forward uncertainty quantification
by: Choi, Hojun, et al.
Published: (2025)

Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization
by: Ghattas, Omar Al, et al.
Published: (2022)

Randomized low-rank approximation of parameter-dependent matrices
by: Kressner, Daniel, et al.
Published: (2023)

An efficient solver based on low-rank approximation and Neumann matrix series for unsteady diffusion-type partial differential equations with random coefficients
by: Zhu, Yujun, et al.
Published: (2026)

Neural active manifolds: nonlinear dimensionality reduction for uncertainty quantification
by: Zanoni, Andrea, et al.
Published: (2024)

Framework for uncertainty quantification of wave-structure interaction in a flume
by: Luo, Xiaoyuan, et al.
Published: (2024)

LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
by: Katz, Sarah, et al.
Published: (2026)

Randomized strong rank-revealing QR for column subset selection and low-rank matrix approximation
by: Grigori, Laura, et al.
Published: (2025)

The temporal domain derivative in inverse acoustic obstacle scattering
by: Knöller, Marvin, et al.
Published: (2025)

Deep learning enhanced cost-aware multi-fidelity uncertainty quantification of a computational model for radiotherapy
by: Vitullo, Piermario, et al.
Published: (2024)

Anisotropic approximation on space-time domains
by: Morin, Pedro, et al.
Published: (2025)

Inference of Heterogeneous Material Properties via Infinite-Dimensional Integrated DIC
by: Kirchhoff, Joseph, et al.
Published: (2024)

Matrices over a Hilbert space and their low-rank approximation
by: Budzinskiy, Stanislav
Published: (2025)

Regularized dynamical parametric approximation
by: Feischl, Michael, et al.
Published: (2024)

The average condition number of most tensor rank decomposition problems is infinite
by: Beltrán, Carlos, et al.
Published: (2019)

Improvements on uncertainty quantification with variational autoencoders
by: Tonini, Andrea, et al.
Published: (2025)

Sharp inverse statements for kernel approximation: Superconvergence and saturation
by: Wenzel, Tizian
Published: (2026)

Ensemble Kalman Filters with Resampling
by: Ghattas, Omar Al, et al.
Published: (2023)

Geometric low-rank approximation of the Zeitlin model of incompressible fluids on the sphere
by: Pagliantini, Cecilia
Published: (2024)