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The main contribution of this paper is to develop a hierarchical Bayesian formulation of PINNs for linear inverse problems, which is called BPINN-IP. The proposed methodology extends PINN to account for prior knowledge on the nature of the…

Machine Learning · Statistics 2026-02-05 Ali Mohammad-Djafari

Uniformly regular equilibrium problems are natural generalizations of abstract equilibrium prob lems and they are defined over the uniformly prox-regular nonconvex sets. Some new efficient implicit methods for solving uniformly regular…

Optimization and Control · Mathematics 2025-02-11 Oday Hazaimah

Normals with unknown parameters (NUP) can be used to convert nontrivial model-based estimation problems into iterations of linear least-squares or Gaussian estimation problems. In this paper, we extend this approach by augmenting factor…

Machine Learning · Statistics 2025-04-24 Yun-Peng Li , Hans-Andrea Loeliger

We prove new results on the existence, non-existence, localization and multiplicity of nontrivial solutions for perturbed Hammerstein integral equations. Our approach is topological and relies on the classical fixed point index. Some of the…

Classical Analysis and ODEs · Mathematics 2016-03-22 Gennaro Infante , Paolamaria Pietramala , F. Adrian F. Tojo

Boundary value problems for integrable nonlinear evolution PDEs formulated on the half-line can be analyzed by the unified method introduced by one of the authors and used extensively in the literature. The implementation of this general…

Analysis of PDEs · Mathematics 2015-05-30 J. Lenells , A. S. Fokas

A multigrid method is proposed for solving nonlinear eigenvalue problems by the finite element method. With this new scheme, solving nonlinear eigenvalue problem is decomposed to a series of solutions of linear boundary value problems on…

Numerical Analysis · Mathematics 2015-01-09 Hehu Xie

In this article we are interested for the numerical study of nonlinear eigenvalue problems. We begin with a review of theoretical results obtained by functional analysis methods, especially for the Schrodinger pencils. Some recall are given…

Numerical Analysis · Mathematics 2016-08-24 Fatima Aboud , Francois Jauberteau , Guy Moebs , Didier Robert

In this note we consider the continuous Galerkin time stepping method of arbitrary order as a possible discretization scheme of nonlinear initial value problems. In addition, we develop and generalize a well known existing result for the…

Numerical Analysis · Mathematics 2021-07-07 Mario Amrein

Effective feature representation is key to the predictive performance of any algorithm. This paper introduces a meta-procedure, called Non-Euclidean Upgrading (NEU), which learns feature maps that are expressive enough to embed the…

Machine Learning · Statistics 2021-05-11 Anastasis Kratsios , Cody Hyndman

In this paper, a novel multigrid method based on Newton iteration is proposed to solve nonlinear eigenvalue problems. Instead of handling the eigenvalue $\lambda$ and eigenfunction $u$ separately, we treat the eigenpair $(\lambda, u)$ as…

Numerical Analysis · Mathematics 2024-04-30 Fei Xu , Manting Xie , Meiling Yue

In this article we study adaptive finite element methods (AFEM) with inexact solvers for a class of semilinear elliptic interface problems. We are particularly interested in nonlinear problems with discontinuous diffusion coefficients, such…

Numerical Analysis · Mathematics 2016-08-24 Michael Holst , Ryan Szypowski , Yunrong Zhu

This paper presents a novel online identification algorithm for nonlinear regression models. The online identification problem is challenging due to the presence of nonlinear structure in the models. Previous works usually ignore the…

Optimization and Control · Mathematics 2022-07-21 Guang-Yong Chen , Min Gan , Jing Chen , Long Chen

We propose an adaptive planewave method for eigenvalue problems in electronic structure calculations. The method combines a priori convergence rates and accurate a posteriori error estimates into an effective way of updating the energy…

Computational Physics · Physics 2021-07-30 Beilei Liu , Huajie Chen , Geneviève Dusson , Jun Fang , Xingyu Gao

This paper addresses the challenge of Neural Field (NeF) generalization, where models must efficiently adapt to new signals given only a few observations. To tackle this, we propose Geometric Neural Process Fields (G-NPF), a probabilistic…

Computer Vision and Pattern Recognition · Computer Science 2025-02-05 Wenzhe Yin , Zehao Xiao , Jiayi Shen , Yunlu Chen , Cees G. M. Snoek , Jan-Jakob Sonke , Efstratios Gavves

Based on the matrix expression of general nonlinear numerical analogues presented by the present author, this paper proposes a novel philosophy of nonlinear computation and analysis. The nonlinear problems are considered an ill-posed linear…

Numerical Analysis · Mathematics 2025-10-20 W. Chen

We study the general $(\boldsymbol{\sigma},\mathbf{p})$-eigenvalue problem of nonnegative tensors introduced by A. Gautier, F. Tudisco, and M. Hein [SIAM J. Matrix Anal. Appl., 40 (2019), pp. 1206--1231], which unifies several well-studied…

Optimization and Control · Mathematics 2025-12-24 Jiefeng Xu , Xueli Bai , Dong-Hui Li

Network scientists have shown that there is great value in studying pairwise interactions between components in a system. From a linear algebra point of view, this involves defining and evaluating functions of the associated adjacency…

Social and Information Networks · Computer Science 2021-08-25 Francesco Tudisco , Desmond J. Higham

This work investigates the application of the Newton's method for the numerical solution of a nonlinear boundary value problem formulated through an ordinary differential equation (ODE). Nonlinear ODEs arise in various mathematical modeling…

The longstanding nonnegative inverse eigenvalue problem (NIEP) is to determine which multisets of complex numbers occur as the spectrum of an entry-wise nonnegative matrix. Although there are some well-known necessary conditions, a solution…

Spectral Theory · Mathematics 2025-08-04 Charles R. Johnson , Pietro Paparella

Envelope methods offer targeted dimension reduction for various models. The overarching goal is to improve efficiency in multivariate parameter estimation by projecting the data onto a lower-dimensional subspace known as the envelope.…

Methodology · Statistics 2022-07-13 Xin Zhang , Kai Deng , Qing Mai