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Parameter identifiability is often requisite to the effective application of mathematical models in the interpretation of biological data, however theory applicable to the study of partial differential equations remains limited. We present…

Analysis of PDEs · Mathematics 2025-04-08 Yurij Salmaniw , Alexander P Browning

Data-driven methods have recently made great progress in the discovery of partial differential equations (PDEs) from spatial-temporal data. However, several challenges remain to be solved, including sparse noisy data, incomplete candidate…

Computational Physics · Physics 2021-09-28 Hao Xu , Dongxiao Zhang , Junsheng Zeng

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

Bilinear inverse problems (BIPs), the resolution of two vectors given their image under a bilinear mapping, arise in many applications. Without further constraints, BIPs are usually ill-posed. In practice, properties of natural signals are…

Information Theory · Computer Science 2015-01-27 Yanjun Li , Kiryung Lee , Yoram Bresler

We present a method to solve initial-boundary value problems for linear and integrable nonlinear differential-difference evolution equations. The method is the discrete version of the one developed by A. S. Fokas to solve initial-boundary…

Exactly Solvable and Integrable Systems · Physics 2009-11-13 Gino Biondini , Guenbo Hwang

The problem of computing differential constraints for a family of evolution PDEs is discussed from a constructive point of view. A new method, based on the existence of generalized characteristics for evolution vector fields, is proposed in…

Mathematical Physics · Physics 2020-08-04 Francesco C. De Vecchi , Paola Morando

Solving an ill-posed linear inverse problem requires knowledge about the underlying signal model. In many applications, this model is a priori unknown and has to be learned from data. However, it is impossible to learn the model using…

Machine Learning · Statistics 2024-10-22 Julián Tachella , Dongdong Chen , Mike Davies

For the first time, some hypersingular nonlinear boundary-value problems with a small parameter~$\varepsilon$ at the highest derivative are described. These problems essentially (qualitatively and quantitatively) differ from the usual…

Analysis of PDEs · Mathematics 2018-02-14 Andrei D. Polyanin , Inna K. Shingareva

We introduce Structure Informed Neural Networks (SINNs), a novel method for solving boundary observation problems involving PDEs. The SINN methodology is a data-driven framework for creating approximate solutions to internal variables on…

Fluid Dynamics · Physics 2023-10-31 Jakub Horsky , Andrew Wynn

This paper is concerned with the output feedback boundary stabilization of general 1-D reaction diffusion PDEs in the presence of an arbitrarily large input delay. We consider the cases of Dirichlet/Neumann/Robin boundary conditions for the…

Optimization and Control · Mathematics 2022-07-13 Hugo Lhachemi , Christophe Prieur

This paper addresses an open inverse problem at the interface of mathematical analysis and spatial ecology: the unique identification of unknown spatial anomalies -- interpreted as zones of habitat degradation -- and their associated…

Analysis of PDEs · Mathematics 2025-12-30 Yuhan Li , Hongyu Liu , Catharine W. K. Lo

Unveiling the underlying governing equations of nonlinear dynamic systems remains a significant challenge. Insufficient prior knowledge hinders the determination of an accurate candidate library, while noisy observations lead to imprecise…

Machine Learning · Computer Science 2024-04-30 Mengge Du , Yuntian Chen , Longfeng Nie , Siyu Lou , Dongxiao Zhang

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

This study presents a novel approach to quantifying uncertainties in Bayesian model updating, which is effective in sparse or single observations. Conventional uncertainty quantification metrics such as the Euclidean and Bhattacharyya…

Applications · Statistics 2024-10-14 Sangwon Lee , Taro Yaoyama , Yuma Matsumoto , Takenori Hida , Tatsuya Itoi

Recovering microscopic couplings directly from data provides a route to solving the inverse problem in statistical field theories, one that complements the traditional-often computationally intractable-forward approach of predicting…

Statistical Mechanics · Physics 2025-11-24 Shreya Shukla , Abhijith Jayakumar , Andrey Y. Lokhov

In this work we investigate the phenomenon of pathwise non-uniqueness for the stochastic incompressible Euler equations with a passive tracer on the whole Euclidean space. The stochastic perturbations are interpreted as a transport noise…

Probability · Mathematics 2026-04-30 Ashish Bawalia , Zdzisław Brzeźniak , Manil T. Mohan

This work considers a nonlinear inverse source problem in a coupled diffusion equation from the terminal observation. Theoretically, under some conditions on problem data, we build the uniqueness theorem for this inverse problem and show…

Numerical Analysis · Mathematics 2025-04-29 Chunlong Sun , Wenlong Zhang , Zhidong Zhang

Motivated by a new kind of initial boundary value problem (IBVP) with a free boundary arising in wave-structure interaction, we propose here a general approach to one-dimensional IBVP as well as transmission problems. For general strictly…

Analysis of PDEs · Mathematics 2018-06-21 Tatsuo Iguchi , David Lannes

This article is devoted to the detection of parameters in anomalous diffusion from a single passive measurement. More precisely, we consider the simultaneous identification of coefficients as well as a time-dependent source term appearing…

Analysis of PDEs · Mathematics 2026-03-10 Maolin Deng , Ali Feizmohammadi , Bangti Jin , Yavar Kian

We introduce an encoder-only approach to learn the evolution operators of large-scale non-linear dynamical systems, such as those describing complex natural phenomena. Evolution operators are particularly well-suited for analyzing systems…

Machine Learning · Computer Science 2025-05-27 Giacomo Turri , Luigi Bonati , Kai Zhu , Massimiliano Pontil , Pietro Novelli
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