Results 221 to 230 of about 37,100 (232)

Robust optimal design of large-scale Bayesian nonlinear inverse problems

arXiv.org
We consider robust optimal experimental design (ROED) for nonlinear Bayesian inverse problems governed by partial differential equations (PDEs). An optimal design is one that maximizes some utility quantifying the quality of the solution of an inverse ...
Abhijit Chowdhary, A. Attia, A. Alexanderian   +2 more
semanticscholar   +1 more source

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography

Computer Methods in Applied Mechanics and Engineering
In this paper, we introduce a novel, data-driven approach for solving high-dimensional Bayesian inverse problems based on partial differential equations (PDEs), called Weak Neural Variational Inference (WNVI).
Vincent C. Scholz, Yaohua Zang, P. Koutsourelakis   +2 more
semanticscholar   +1 more source

Some non-linear systems of PDEs related to inverse problems in conductivity

Calculus of Variations and Partial Differential Equations, 2021
Faustino Maestre, P. Pedregal
semanticscholar   +1 more source

Physics-informed neural networks for inverse problems in structural dynamics

Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring
This study introduces an innovative approach that employs Physics-Informed Neural Networks (PINNs) to address inverse problems in structural analysis.
Rafael de O. Teloli, Mael Bigot, Lucas Coelho, Emmanuel Ramasso, Roberta Tittarelli, Patrice Le Moal, M. Ouisse   +6 more
semanticscholar   +1 more source

Fast and Scalable FFT-Based GPU-Accelerated Algorithms for Block-Triangular Toeplitz Matrices with Application to Linear Inverse Problems Governed by Autonomous Dynamical Systems

SIAM Journal on Scientific Computing
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 problems ...
Sreeram Venkat, Milinda Fernando, Stefan Henneking, Omar Ghattas   +3 more
semanticscholar   +1 more source

A Score-based Generative Solver for PDE-constrained Inverse Problems with Complex Priors

arXiv.org
In the field of inverse estimation for systems modeled by partial differential equations (PDEs), challenges arise when estimating high- (or even infinite-) dimensional parameters.
Yankun Hong, Harshit Bansal, K. Veroy
semanticscholar   +1 more source

Computational Aspects of Solving Inverse Problems for Elliptic PDEs on Perforated Domains Using the Collage Method

, 2016
H. Kunze, D. Torre
semanticscholar   +1 more source

A Review of Physics Informed Neural Networks for Multiscale Analysis and Inverse Problems

Multiscale Science and Engineering
Dongjin Kim, Jaewook Lee
semanticscholar   +1 more source

Extreme-scale UQ for Bayesian inverse problems governed by PDEs

2012 International Conference for High Performance Computing, Networking, Storage and Analysis, 2012
Tan Bui-Thanh, Carsten Burstedde, Omar Ghattas, James Martin, Georg Stadler, Lucas C. Wilcox   +5 more
openaire   +1 more source

ODE-DPS: ODE-Based Diffusion Posterior Sampling for Linear Inverse Problems in Partial Differential Equation

Journal of Scientific Computing
Enze Jiang, Jishen Peng, Zheng Ma, Xiong-bin Yan   +3 more
semanticscholar   +1 more source