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In this paper, two high order complex contour discretization methods are proposed to simulate wave propagation in locally perturbed periodic closed waveguides. As is well known the problem is not always uniquely solvable due to the…

Numerical Analysis · Mathematics 2022-07-27 Ruming Zhang

We consider the eigenvalue problem of certain kind of non-compact linear operators given as the sum of a multiplication and a kernel operator. A degenerate kernel method is used to approximate isolated eigenvalues. It is shown that entries…

Numerical Analysis · Mathematics 2008-10-18 Hassan Majidian , Esmail Babolian

We extend the diffusion-map formalism to data sets that are induced by asymmetric kernels. Analytical convergence results of the resulting expansion are proved, and an algorithm is proposed to perform the dimensional reduction. In this work…

Machine Learning · Computer Science 2024-01-24 Alvaro Almeida Gomez , Antonio Silva Neto , Jorge zubelli

We consider the inverse problem of the simultaneous reconstruction of the dielectric permittivity and magnetic permeability functions of the Maxwell's system in 3D with limited boundary observations of the electric field. The theoretical…

Numerical Analysis · Mathematics 2015-12-03 Larisa Beilina , Michel Cristofol , Kati Niinimäki

Traditionally, kernel methods rely on the representer theorem which states that the solution to a learning problem is obtained as a linear combination of the data mapped into the reproducing kernel Hilbert space (RKHS). While elegant from…

Machine Learning · Computer Science 2021-08-30 Riikka Huusari , Sahely Bhadra , Cécile Capponi , Hachem Kadri , Juho Rousu

A type of parallel augmented subspace scheme for eigenvalue problems is proposed by using coarse space in the multigrid method. With the help of coarse space in multigrid method, solving the eigenvalue problem in the finest space is…

Numerical Analysis · Mathematics 2020-08-19 Fei Xu , Hehu Xie , Ning Zhang

Kernel methods for solving partial differential equations on surfaces have the advantage that those methods work intrinsically on the surface and yield high approximation rates if the solution to the partial differential equation is smooth…

Numerical Analysis · Mathematics 2024-10-04 Thomas Hangelbroek , Christian Rieger

In this work, the fast-convolving reproducing kernel particle method (FC-RKPM) is introduced. This method is hundreds to millions of times faster than the traditional RKPM for 3D meshfree simulations. In this approach, the meshfree…

Numerical Analysis · Mathematics 2024-04-01 Siavash Jafarzadeh , Michael Hillman

We propose and analyze a new discretization technique for a linear-quadratic optimal control problem involving the fractional powers of a symmetric and uniformly elliptic second oder operator; control constraints are considered. Since these…

Numerical Analysis · Mathematics 2016-07-08 Enrique Otarola

We present a finite-difference integration algorithm for solution of a system of differential equations containing a diffusion equation with nonlinear terms. The approach is based on Crank-Nicolson method with predictor-corrector algorithm…

Computational Physics · Physics 2019-07-05 V. P. Lipp , B. Rethfeld , M. E. Garcia , D. S. Ivanov

Finite element methods for electromagnetic problems modeled by Maxwell-type equations are highly sensitive to the conformity of approximation spaces, and non-conforming methods may cause loss of convergence. This fact leads to an essential…

Numerical Analysis · Mathematics 2023-04-05 Shuhao Cao , Long Chen , Ruchi Guo

Motivated by problems where the response is needed at select localized regions in a large computational domain, we devise a novel finite element discretization that results in exponential convergence at pre-selected points. The two key…

Numerical Analysis · Mathematics 2016-08-03 Murthy N. Guddati , Vladimir Druskin , Ali Vaziri Astaneh

Signature kernels have emerged as a powerful tool within kernel methods for sequential data. In the paper "The Signature Kernel is the solution of a Goursat PDE", the authors identify a kernel trick that demonstrates that, for continuously…

Numerical Analysis · Mathematics 2026-01-19 Thomas Cass , Francesco Piatti , Jeffrey Pei

We provide a new method to approximate a (possibly discontinuous) function using Christoffel-Darboux kernels. Our knowledge about the unknown multivariate function is in terms of finitely many moments of the Young measure supported on the…

Optimization and Control · Mathematics 2021-04-09 Swann Marx , Edouard Pauwels , Tillmann Weisser , Didier Henrion , Jean Lasserre

We study Sparse Multiple Kernel Learning (SMKL), which is the problem of selecting a sparse convex combination of prespecified kernels for support vector binary classification. Unlike prevailing l1 regularized approaches that approximate a…

Machine Learning · Statistics 2025-12-03 Dimitris Bertsimas , Caio de Prospero Iglesias , Nicholas A. G. Johnson

In this paper we design, analyse and test domain decomposition methods for linear systems of equations arising from conforming finite element discretisations of positive Maxwell-type equations, namely for $\mathbf{H}(\mathbf{curl})$…

Numerical Analysis · Mathematics 2025-10-28 Niall Bootland , Victorita Dolean , Frédéric Nataf , Pierre-Henri Tournier

We study the bifurcation diagram of the Onsager free-energy func- tional for liquid crystals with orientation parameter on the sphere in three dimensions. In particular, we concentrate on a very general class of two- body interaction…

Analysis of PDEs · Mathematics 2015-09-09 Michaela Andrea Christine Vollmer

The modified Maxwell's Stekloff eigenvalue problem arises recently from the inverse electromagnetic scattering theory for inhomogeneous media. This paper contains a rigorous analysis of both the eigenvalue problem and the associated source…

Numerical Analysis · Mathematics 2020-04-10 Bo Gong , Jiguang Sun , Xinming Wu

Panel-based, kernel-split quadrature is currently one of the most efficient methods available for accurate evaluation of singular and nearly singular layer potentials in two dimensions. However, it can fail completely for the layer…

Numerical Analysis · Mathematics 2022-01-20 Fredrik Fryklund , Ludvig af Klinteberg , Anna-Karin Tornberg

Kernel-based methods in Numerical Analysis have the advantage of yielding optimal recovery processes in the "native" Hilbert space $\calh$ in which they are reproducing. Continuous kernels on compact domains have an expansion into…

Numerical Analysis · Mathematics 2018-10-09 Gabriele Santin , Robert Schaback
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