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In this paper we investigate the Bayesian approach to inverse Robin problems. These are problems for certain elliptic boundary value problems of determining a Robin coefficient on a hidden part of the boundary from Cauchy data on the…

Statistics Theory · Mathematics 2023-11-30 Aksel Kaastrup Rasmussen , Fanny Seizilles , Mark Girolami , Ieva Kazlauskaite

Under consideration are mathematical models of heat and mass transfer. We study inverse problems of recovering lower-order coefficients in a second order parabolic equation. The coefficients are representable in the form of a finite…

Analysis of PDEs · Mathematics 2024-12-23 S. G. Pyatkov , O. A. Soldatov

This article is concerned with the derivation of numerical reconstruction schemes for the inverse moving source problem on determining source profiles in (time-fractional) evolution equations. As a continuation of the theoretical result on…

Numerical Analysis · Mathematics 2021-03-26 Yikan Liu

In this paper we consider second order evolution equations with unbounded dynamic feedbacks. Under a regularity assumption we show that observability properties for the undamped problem imply decay estimates for the damped problem. We…

Analysis of PDEs · Mathematics 2014-07-14 Zainab Abbas , Kais Ammari , Denis Mercier

We provide a necessary and sufficient condition for a rough control driving a differential equation to be reconstructable, to some order, from observing the resulting controlled evolution. Physical examples and applications in stochastic…

Probability · Mathematics 2014-11-17 I. Bailleul , J. Diehl

In this paper we examine a mutual control problem for systems of two abstract evolution equations subject to a proportionality final condition. Related observability and semi-observability problems are discussed. The analysis employs a…

Analysis of PDEs · Mathematics 2024-09-17 Radu Precup , Andrei Stan

Observability inequalities on lattice points are established for non-negative solutions of the heat equation with potentials in the whole space. As applications, some controllability results of heat equations are derived by the…

Optimization and Control · Mathematics 2018-12-04 Ming Wang , Can Zhang , Liang Zhang

The paper studies inverse problems of determining unknown coefficients in various semi-linear and quasi-linear wave equations. We introduce a method to solve inverse problems for non-linear equations using interaction of three waves, that…

Analysis of PDEs · Mathematics 2023-05-10 Ali Feizmohammadi , Matti Lassas , Lauri Oksanen

In the paper we consider the linear inverse problem that consists in recovering the initial state in a first order evolution equation generated by a skew-adjoint operator. We studied the well-posedness of the inversion in terms of the…

Analysis of PDEs · Mathematics 2018-12-20 Kaïs Ammari , Faouzi Triki

A variety of approaches has been developed to deal with uncertain optimization problems. Often, they start with a given set of uncertainties and then try to minimize the influence of these uncertainties. Depending on the approach used, the…

Optimization and Control · Mathematics 2023-07-14 Holger Berthold , Till Heller , Tobias Seidel

In this paper, we propose and study several inverse problems of determining unknown parameters in nonlocal nonlinear coupled PDE systems, including the potentials, nonlinear interaction functions and time-fractional orders. In these coupled…

Analysis of PDEs · Mathematics 2024-07-23 Ming-Hui Ding , Hongyu Liu , Catharine W. K. Lo

Some iterative techniques are defined to solve reversible inverse problems and a common formulation is explained. Numerical improvements are suggested and tests validate the methods.

Optimization and Control · Mathematics 2011-12-06 Sébastien Marinesque

A proposal is made for a mathematically unambiguous treatment of evolution in the presence of closed timelike curves. In constrast to other proposals for handling the naively nonunitary evolution that is often present in such situations,…

General Relativity and Quantum Cosmology · Physics 2010-11-01 Arlen Anderson

Uncertainty quantification by ensemble learning is explored in terms of an application from computational optical form measurements. The application requires to solve a large-scale, nonlinear inverse problem. Ensemble learning is used to…

Machine Learning · Computer Science 2021-03-03 Lara Hoffmann , Ines Fortmeier , Clemens Elster

The unfolding problem formulation for correcting experimental data distortions due to finite resolution and limited detector acceptance is discussed. A novel validation of the problem solution is proposed. Attention is drawn to fact that…

Data Analysis, Statistics and Probability · Physics 2016-04-08 Nikolai Gagunashvili

We study a nonlinear evolutionary partial differential equation that can be viewed as a generalization of the heat equation where the temperature gradient is a~priori bounded but the heat flux provides merely \mbox{$L^1$-coercivity}.…

Analysis of PDEs · Mathematics 2021-03-01 Miroslav Bulíček , David Hruška , Josef Málek

The invariant subspace method is refined to present more unity and more diversity of exact solutions to evolution equations. The key idea is to take subspaces of solutions to linear ordinary differential equations as invariant subspaces…

Exactly Solvable and Integrable Systems · Physics 2015-06-04 Wen-Xiu Ma

The need to blend observational data and mathematical models arises in many applications and leads naturally to inverse problems. Parameters appearing in the model, such as constitutive tensors, initial conditions, boundary conditions, and…

Statistics Theory · Mathematics 2010-09-16 J. Nolen , G. A. Pavliotis , A. M. Stuart

A general shape identification inverse problem is studied in a Bayesian framework. This problem requires the determination of the unknown shape of a domain in the Euclidean space from finite-dimensional observation data with some Gaussian…

Statistics Theory · Mathematics 2020-02-19 Hajime Kawakami

We propose to solve inverse problems involving the temporal evolution of physics systems by leveraging recent advances from diffusion models. Our method moves the system's current state backward in time step by step by combining an…

Machine Learning · Computer Science 2023-12-06 Benjamin J. Holzschuh , Simona Vegetti , Nils Thuerey