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This work addresses inverse linear optimization where the goal is to infer the unknown cost vector of a linear program. Specifically, we consider the data-driven setting in which the available data are noisy observations of optimal…

Optimization and Control · Mathematics 2021-12-07 Rishabh Gupta , Qi Zhang

The inverse problem associated with electrochemical impedance spectroscopy requiring the solution of a Fredholm integral equation of the first kind is considered. If the underlying physical model is not clearly determined, the inverse…

Numerical Analysis · Mathematics 2022-08-16 Jakob Hansen , Jarom Hogue , Grant Sander , Rosemary Renaut , Sudeep Popat

This work introduces a non-intrusive model reduction approach for learning reduced models from partially observed state trajectories of high-dimensional dynamical systems. The proposed approach compensates for the loss of information due to…

Machine Learning · Computer Science 2021-03-29 Wayne Isaac Tan Uy , Benjamin Peherstorfer

An adaptive algorithm, based on residual type a posteriori indicators of errors measured in $L^{\infty}(L^2)$ and $L^2(L^2)$ norms, for a numerical scheme consisting of implicit Euler method in time and discontinuous Galerkin method in…

Numerical Analysis · Mathematics 2013-03-12 Emmanuil H. Georgoulis , Juha M. Virtanen

We study an inverse problem for the time-dependent Maxwell system in an inhomogeneous and anisotropic medium. The objective is to recover the initial electric field $\mathbf{E}_0$ in a bounded domain $\Omega \subset \mathbb{R}^3$, using…

Numerical Analysis · Mathematics 2025-06-27 Thuy T. Le , Cong B. Van , Trong D. Dang , Loc H. Nguyen

In this paper we introduce a numerical method for nonlinear parabolic PDEs that combines operator splitting with deep learning. It divides the PDE approximation problem into a sequence of separate learning problems. Since the computational…

Numerical Analysis · Mathematics 2021-10-12 Christian Beck , Sebastian Becker , Patrick Cheridito , Arnulf Jentzen , Ariel Neufeld

Random coupled parabolic partial differential models are solved numerically using random cosine Fourier transform together with non Gaussian random numerical integration that capture the highly oscillatory behavior of the involved…

Numerical Analysis · Mathematics 2025-01-28 M. -C. Casabán , R. Company , V. N. Egorova , L. Jódar

We consider algebraic iterative reconstruction methods with applications in image reconstruction. In particular, we are concerned with methods based on an unmatched projector/backprojector pair; i.e., the backprojector is not the exact…

Numerical Analysis · Mathematics 2019-02-14 Yiqiu Dong , Per Christian Hansen , Michiel E. Hochstenbach , Nicolai Andre Brogaard Riis

The variational approach to fracture is effective for simulating the nucleation and propagation of complex crack patterns, but is computationally demanding. The model is a strongly nonlinear non-convex variational inequality that demands…

Numerical Analysis · Mathematics 2016-05-18 Patrick E. Farrell , Corrado Maurini

In this paper, we investigate the inverse problem on determining the spatial component of the source term in a hyperbolic equation with time-dependent principal part. Based on a newly established Carleman estimate for general hyperbolic…

Analysis of PDEs · Mathematics 2019-04-12 Daijun Jiang , Yikan Liu , Masahiro Yamamoto

In this paper we will consider the peridynamic equation of motion which is described by a second order in time partial integro-differential equation. This equation has recently received great attention in several fields of Engineering…

An efficient nonlinear contrast source inversion scheme for electromagnetic imaging of sparse two-dimensional investigation domains is proposed. To avoid generating a sequence of linear sparse optimization problems, the non-linearity is…

Signal Processing · Electrical Eng. & Systems 2021-04-13 Ali I. Sandhu , Abdulla Desmal , Hakan Bagci

In this paper, we propose novel proper orthogonal decomposition (POD)--based model reduction methods that effectively address the issue of inverse crime in solving parabolic inverse problems. Both the inverse initial value problems and…

Numerical Analysis · Mathematics 2024-06-05 Wenlong Zhang , Zhiwen Zhang

This paper proposes a new and efficient numerical algorithm for recovering the damping coefficient from the spectrum of a damped wave operator, which is a classical Borg-Levinson inverse spectral problem. The algorithm is based on inverting…

Numerical Analysis · Mathematics 2020-08-12 Gang Bao , Xiang Xu , Jian Zhai

We propose two methods to find a proper shift parameter in the shift-and-invert method for computing matrix exponential matrix-vector products. These methods are useful in the case of matrix exponential action has to be computed for a…

Numerical Analysis · Mathematics 2019-10-01 Alexandr Katrutsa , Mike Botchev , Ivan Oseledets

We propose an efficient residual minimization technique for the nonlinear model-order reduction of parameterized hyperbolic partial differential equations. Our nonlinear approximation space is a span of snapshots evaluated on a shifted…

Numerical Analysis · Mathematics 2021-01-21 Neeraj Sarna , Sara Grundel

Deep learning-based models have demonstrated remarkable success in solving illposed inverse problems; however, many fail to strictly adhere to the physical constraints imposed by the measurement process. In this work, we introduce a…

Machine Learning · Computer Science 2025-05-22 Jorge Bacca

We consider a parabolic equation in a bounded domain $\OOO$ over a time interval $(0,T)$ with the homogeneous Neumann boundary condition. We arbitrarily choose a subboundary $\Gamma \subset \ppp\OOO$. Then, we discuss an inverse problem of…

Analysis of PDEs · Mathematics 2022-11-23 O. Imanuvilov , M. Yamamoto

This paper presents a concurrent global-local numerical method for solving multiscale parabolic equations in divergence form. The proposed method employs hybrid coefficient to provide accurate macroscopic information while preserving…

Numerical Analysis · Mathematics 2026-04-14 Yulei Liao , Yang Liu , Pingbing Ming

This note introduces a regression technique for finding a class of nonlinear integro-differential operators from data. The method parametrizes the spatial operator with neural networks and Fourier transforms such that it can fit a class of…

Machine Learning · Computer Science 2018-10-22 Ravi G. Patel , Olivier Desjardins