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Initial-boundary value problems for complex sine-Gordon and sine-Gordon equations in a semi--strip are treated. The evolution of the Weyl function and a uniqueness result are obtained for complex sine-Gordon equation. The evolution of the…

Exactly Solvable and Integrable Systems · Physics 2013-01-30 Alexander Sakhnovich

These notes provide a self-contained introduction to kernel methods and their geometric foundations in machine learning. Starting from the construction of Hilbert spaces, we develop the theory of positive definite kernels, reproducing…

This paper proposes and studies a numerical method for approximation of posterior expectations based on interpolation with a Stein reproducing kernel. Finite-sample-size bounds on the approximation error are established for posterior…

Statistics Theory · Mathematics 2022-01-12 Alessandro Barp , Chris. J. Oates , Emilio Porcu , Mark Girolami

In this paper, we develop a numerical method for the computation of (quasi-)resonances in spherical symmetric, heterogeneous Helmholtz problems with piecewise smooth refractive index. Our focus lies in resonances very close to the real…

Numerical Analysis · Mathematics 2026-02-19 Bouchra Bensiali , Stefan Sauter

We analyse the convergence of sampling algorithms for functions in reproducing kernel Hilbert spaces (RKHS). To this end, we discuss approximation properties of kernel regression under minimalistic assumptions on both the kernel and the…

Machine Learning · Statistics 2025-04-21 Armin Iske

We derive exact solutions to the sine--Gordon equation describing localized structures on the background of librational and rotational travelling waves. In the case of librational waves, the exact solution represents a localized spike in…

Exactly Solvable and Integrable Systems · Physics 2021-03-17 Dmitry E. Pelinovsky , Robert E. White

We study spacetimes that lead to a separable Klein-Gordon equation in a general dimension. We introduce an ansatz for the metric in higher dimensions motivated by analogical work by Carter in four dimensions and find solutions of the…

General Relativity and Quantum Cosmology · Physics 2016-02-10 Ivan Kolar , Pavel Krtous

We demonstrate a systematic method for solving the Hamilton-Jacobi equation for general relativity with the inclusion of matter fields. The generating functional is expanded in a series of spatial gradients. Each term is manifestly…

General Relativity and Quantum Cosmology · Physics 2010-11-01 J. Parry D. S. Salopek , J. M. Stewart

This paper is concerned with a class of approximate non-linear transformations that compress solutions of the (generalized) sinh-Gordon equation into parametrically small regions in two-dimensional spacetime. Given the sinh-Gordon field…

High Energy Physics - Theory · Physics 2021-06-25 David Vegh

We present an efficient second-order finite difference scheme for solving the 2D sine-Gordon equation, which can inherit the discrete energy conservation for the undamped model theoretically. Due to the semi-implicit treatment for the…

Numerical Analysis · Mathematics 2017-06-28 Xiaorong Kang , Wenqiang Feng , Kelong Cheng , Chunxiang Guo

We consider a completely integrable lattice regularization of the sine-Gordon model with discrete space and continuous time. We derive a determinant representation for a correlation function which in the continuum limit turns into the…

High Energy Physics - Theory · Physics 2008-11-26 Fabian H. L. Essler , Holger Frahm , Alexander R. Its , Vladimir E. Korepin

We develop a theory of the Klein-Gordon equation on curved spacetimes. Our main tool is the method of (non-autonomous) evolution equations on Hilbert spaces. This approach allows us to treat low regularity of the metric, of the…

Mathematical Physics · Physics 2019-05-15 Jan Dereziński , Daniel Siemssen

The study of the electrodynamics of static, axisymmetric and force-free Kerr magnetospheres relies vastly on solutions of the so called relativistic Grad-Shafranov equation (GSE). Different numerical approaches to the solution of the GSE…

High Energy Astrophysical Phenomena · Physics 2018-05-08 J. F. Mahlmann , P. Cerdá-Durán , M. A. Aloy

Suppose that $Y$ is a scalar and $X$ is a second-order stochastic process, where $Y$ and $X$ are conditionally independent given the random variables $\xi_1,...,\xi_p$ which belong to the closed span $L_X^2$ of $X$. This paper investigates…

Statistics Theory · Mathematics 2009-04-02 Tailen Hsing , Haobo Ren

Kernel methods approximate nonlinear maps in a data-driven manner by projecting the target map onto a finite-dimensional Hilbert space called the solution space. Traditionally, this space is a subspace of a fixed ambient reproducing kernel…

Numerical Analysis · Mathematics 2026-01-30 Tamás Dózsa , Andrea Angino , Zoltán Szabó , József Bokor , Matthias Voigt

We present a general framework to learn functions in tensor product reproducing kernel Hilbert spaces (TP-RKHSs). The methodology is based on a novel representer theorem suitable for existing as well as new spectral penalties for tensors.…

Machine Learning · Computer Science 2013-10-21 Marco Signoretto , Lieven De Lathauwer , Johan A. K. Suykens

The goal of this paper is to give a representation-theoretic interpretation of the sine-Gordon equation. We consider a vertex operator representation of affine Kac-Moody algebra \hat{sl_2} on the space of differential operators. In this…

High Energy Physics - Theory · Physics 2007-05-23 Yuly Billig

We solve the relativistic Klein--Gordon equation for a light particle gravitationally bound to a heavy central mass, with the gravitational interaction prescribed by the metric of a spherically symmetric space-time. Metrics are considered…

General Relativity and Quantum Cosmology · Physics 2018-06-13 R. D. Lehn , S. S. Chabysheva , J. R. Hiller

This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing…

Machine Learning · Statistics 2021-06-17 Benyamin Ghojogh , Ali Ghodsi , Fakhri Karray , Mark Crowley

We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it admits upper and lower approximations leading to various…

Machine Learning · Statistics 2019-05-15 Alberto Bietti , Grégoire Mialon , Dexiong Chen , Julien Mairal
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