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We study the convergence of random function iterations for finding an invariant measure of the corresponding Markov operator. We call the problem of finding such an invariant measure the stochastic fixed point problem. This generalizes…

Optimization and Control · Mathematics 2024-04-16 Neal Hermer , D. Russell Luke , Anja Sturm

The long-time evolution of extreme mass-ratio inspiral systems requires minimal phase and dispersion errors to accurately compute far-field waveforms, while high accuracy is essential near the smaller black hole (modeled as a Dirac delta…

General Relativity and Quantum Cosmology · Physics 2025-09-16 Manas Vishal , Scott E. Field , Sigal Gottlieb , Jennifer Ryan

We formulate a stabilized quasi-optimal Petrov-Galerkin method for singularly perturbed convection-diffusion problems based on the variational multiscale method. The stabilization is of Petrov-Galerkin type with a standard finite element…

Numerical Analysis · Mathematics 2016-06-16 Guanglian Li , Daniel Peterseim , Mira Schedensack

In recent years, stochastic effects have become increasingly relevant for describing fluid behaviour, particularly in the context of turbulence. The most important model for inviscid fluids in computational fluid dynamics are the Euler…

Numerical Analysis · Mathematics 2024-12-11 Dominic Breit , Thamsanqa Castern Moyo , Philipp Öffner

Training nonlinear parametrizations such as deep neural networks to numerically approximate solutions of partial differential equations is often based on minimizing a loss that includes the residual, which is analytically available in…

Numerical Analysis · Mathematics 2023-06-28 Yuxiao Wen , Eric Vanden-Eijnden , Benjamin Peherstorfer

We introduce a family of discontinuous Galerkin methods to approximate the eigenvalues and eigenfunctions of a Stokes-Brinkman type of problem based in the interior penalty strategy. Under the standard assumptions on the meshes and a…

Numerical Analysis · Mathematics 2025-07-17 Felipe Lepe , Gonzalo Rivera , Jesus Vellojin

In this paper, we concentrate on the superconvergence of the local discontinuous Galerkin method with generalized numerical fluxes for one-dimensional linear time-dependent fourth-order equations. The adjustable numerical viscosity of the…

Numerical Analysis · Mathematics 2024-09-24 Linhui Li , Xiong Meng , Boying Wu

We propose to study unitary matrix ensembles defined in terms of unitary stochastic transition matrices associated with Markov processes on graphs. We argue that the spectral statistics of such an ensemble (after ensemble averaging) depends…

Chaotic Dynamics · Physics 2009-11-07 Gregor Tanner

This paper focuses on the adaptive discontinuous Galerkin (DG) methods for the tempered fractional (convection) diffusion equations. The DG schemes with interior penalty for the diffusion term and numerical flux for the convection term are…

Numerical Analysis · Mathematics 2020-06-16 Xudong Wang , Weihua Deng

In this paper, a non-polynomial spectral Petrov-Galerkin method and associated collocation method for substantial fractional differential equations (FDEs) are proposed, analyzed, and tested. We extend a class of generalized Laguerre…

Numerical Analysis · Mathematics 2014-08-27 Can Huang , Qingshuo Song , Zhimin Zhang

In this paper we provide new quantum algorithms with polynomial speed-up for a range of problems for which no such results were known, or we improve previous algorithms. First, we consider the approximation of the frequency moments $F_k$ of…

Quantum Physics · Physics 2019-07-08 Yassine Hamoudi , Frédéric Magniez

A novel mixed spectral-Galerkin method based on generalized ball polynomials is proposed for solving the biharmonic equation on a unit ball. By introducing an auxiliary variable to decouple the biharmonic equation into a system of…

Numerical Analysis · Mathematics 2026-05-29 Mengxue Gao , Bing Su , Jianwei Zhou

The use of spectral proper orthogonal decomposition (SPOD) to construct low-order models for broadband turbulent flows is explored. The choice of SPOD modes as basis vectors is motivated by their optimality and space-time coherence…

Fluid Dynamics · Physics 2021-09-22 Tianyi Chu , Oliver T. Schmidt

First passage distributions of semi-Markov processes are of interest in fields such as reliability, survival analysis, and many others. The problem of finding or computing first passage distributions is, in general, quite challenging. We…

Methodology · Statistics 2020-08-10 Richard L. Warr

We study a class of stochastic semilinear damped wave equations driven by additive Wiener noise. Owing to the damping term, under appropriate conditions on the nonlinearity, the solution admits a unique invariant distribution. We apply…

Numerical Analysis · Mathematics 2023-06-27 Ziyi Lei , Charles-Edouard Bréhier , Siqing Gan

We present a practical and effective method for rigorously estimating quantities associated to top eigenvalues of transfer operators to very high precision. The method combines explicit error bounds of the Lagrange-Chebyshev approximation…

Dynamical Systems · Mathematics 2023-08-09 Mark Pollicott , Julia Slipantschuk

We analyze the properties of degree-preserving Markov chains based on elementary edge switchings in undirected and directed graphs. We give exact yet simple formulas for the mobility of a graph (the number of possible moves) in terms of its…

Disordered Systems and Neural Networks · Physics 2012-03-12 E. S. Roberts , A. Annibale , A. C. C. Coolen

We consider the initial/boundary value problem for a diffusion equation involving multiple time-fractional derivatives on a bounded convex polyhedral domain. We analyze a space semidiscrete scheme based on the standard Galerkin finite…

Numerical Analysis · Mathematics 2015-06-18 Bangti Jin , Raytcho Lazarov , Yikan Liu , Zhi Zhou

These lecture notes introduce the statistical analysis of continuous-time generative models built from Markov dynamics. We begin with the stochastic-calculus foundations of score-based diffusion models, including time reversal, score…

Statistics Theory · Mathematics 2026-04-27 Eddie Aamari , Arthur Stéphanovitch

This paper investigates Gaussian Markov random field approximations to nonstationary Gaussian fields using graph representations of stochastic partial differential equations. We establish approximation error guarantees building on the…

Methodology · Statistics 2021-04-28 Daniel Sanz-Alonso , Ruiyi Yang
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