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Kernel approximation with exponentials is useful in many problems with convolution quadrature and particle interactions such as integral-differential equations, molecular dynamics and machine learning. This paper proposes a weighted…

Computational Physics · Physics 2025-05-07 Yuanshen Lin , Zhenli Xu , Yusu Zhang , Qi Zhou

The Kirchhoff integral is a fundamental integral in scattering theory, appearing in both the Kirchhoff approximation, as well as the small slope approximation. In this work, a functional Taylor series approximation to…

Computational Physics · Physics 2021-06-16 Derek R. Olson

A novel approach to wall modeling for the incompressible Navier-Stokes equations including flows of moderate and large Reynolds numbers is presented. The basic idea is that a problem-tailored function space allows prediction of turbulent…

Fluid Dynamics · Physics 2016-04-18 Benjamin Krank , Wolfgang A. Wall

We study properties of an attractive-repulsive energy functional based on power-kernels, which can be used for halftoning of images. In the first part of this work, using a variational framework for probability measures, we examine…

Analysis of PDEs · Mathematics 2013-10-07 Jan-Christian Hütter

The paper addresses an escape of a classical particle from a potential well under harmonic forcing. Most dangerous/efficient escape dynamics reveals itself in conditions of 1:1 resonance and can be described in the framework of isolated…

Dynamical Systems · Mathematics 2022-03-02 Pavel Kravetc , Oleg Gendelman

In this work, an efficient approximation scheme has been proposed for getting accurate approximate solution of nonlinear partial differential equations with constant or variable coefficients satisfying initial conditions in a series of…

Analysis of PDEs · Mathematics 2020-09-04 Prakash Kumar Das , M. M. Panja

The Sakiadis boundary layer induced by a moving wall in a semi-infinite fluid domain is a fundamental laminar flow field relevant to high speed coating processes. This work provides an analytical solution to the boundary layer problem for…

Exponential integrability properties of numerical approximations are a key tool for establishing positive rates of strong and numerically weak convergence for a large class of nonlinear stochastic differential equations. It turns out that…

Numerical Analysis · Mathematics 2020-08-10 Martin Hutzenthaler , Arnulf Jentzen , Xiaojie Wang

Eigenvalues and eigenfunctions of Mathieu's equation are found in the short wavelength limit using a uniform approximation (method of comparison with a `known' equation having the same classical turning point structure) applied in Fourier…

Quantum Physics · Physics 2011-06-09 Duncan H. J. O'Dell

We consider approximating analytic functions on the interval $[-1,1]$ from their values at a set of $m+1$ equispaced nodes. A result of Platte, Trefethen \& Kuijlaars states that fast and stable approximation from equispaced samples is…

Numerical Analysis · Mathematics 2022-03-08 Ben Adcock , Alexei Shadrin

We introduce a numerical method for the approximation of functions which are analytic on compact intervals, except at the endpoints. This method is based on variable transforms using particular parametrized exponential and…

Numerical Analysis · Mathematics 2016-09-06 Ben Adcock , Jésus Martín-Vaquero , Mark Richardson

We discuss two simple variational approaches to quantum wells. The trial harmonic functions analyzed in an earlier paper give reasonable results for all well depths and are particularly suitable for deep wells. On the other hand, the…

Quantum Physics · Physics 2012-04-05 Francisco M. Fernández

A compact and accurate solution method is provided for problems whose infinite power series solution diverges and/or whose series coefficients are only known up to a finite order. The method only requires that either the power series…

Numerical Analysis · Mathematics 2017-02-09 Nathaniel S. Barlow , Christopher R. Stanton , Nicole Hill , Steven J. Weinstein , Allyssa G. Cio

In this paper we propose a novel way to integrate time-evolving partial differential equations that contain nonlinear advection and stiff linear operators, combining exponential integration techniques and semi-Lagrangian methods. The…

Computational Physics · Physics 2019-11-05 Pedro da Silva Peixoto , Martin Schreiber

We identify a set of "energy" functionals on the space of metrics in a given Kaehler class on a Calabi-Yau manifold, which are bounded below and minimized uniquely on the Ricci-flat metric in that class. Using these functionals, we recast…

High Energy Physics - Theory · Physics 2014-05-19 Matthew Headrick , Ali Nassar

A conjectured relation between Ramanujan's asymptotic approximations to the exponential function and the exponential integral is established. The proof involves Stirling numbers, second-order Eulerian numbers, modifications of both of…

Number Theory · Mathematics 2023-02-14 Cormac O'Sullivan

It is pointed out that Hantush's \emph{M} function, commonly used in groundwater pumping modeling, is identical to the function known in the problems of heat conduction in the ground. A modified Hantush function $E$ used in the steady-state…

In this work a general approach to compute a compressed representation of the exponential $\exp(h)$ of a high-dimensional function $h$ is presented. Such exponential functions play an important role in several problems in Uncertainty…

Numerical Analysis · Mathematics 2023-02-22 Martin Eigel , Nando Farchmin , Sebastian Heidenreich , Philipp Trunschke

We propose a general method for optimization with semi-infinite constraints that involve a linear combination of functions, focusing on the case of the exponential function. Each function is lower and upper bounded on sub-intervals by…

Optimization and Control · Mathematics 2014-01-13 Bogdan Dumitrescu , Bogdan C. Sicleru , Florin Avram

A three-hidden-layer neural network with super approximation power is introduced. This network is built with the floor function ($\lfloor x\rfloor$), the exponential function ($2^x$), the step function ($1_{x\geq 0}$), or their compositions…

Machine Learning · Computer Science 2021-04-27 Zuowei Shen , Haizhao Yang , Shijun Zhang