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The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators allows for formal statistical quantification of the error due to discretisation in the numerical context. Competing…

Methodology · Statistics 2018-05-23 Junyang Wang , Jon Cockayne , Chris Oates

The eigenvalues and eigenfunctions of the Stokes operator have been the subject of intense analytical investigation and have applications in the study and simulation of the Navier-Stokes equations. As the Stokes operator is a fourth-order…

Numerical Analysis · Mathematics 2019-04-17 Travis Askham , Manas Rachh

In this paper, we propose a new covering technique localized for the trajectories of SGD. This localization provides an algorithm-specific complexity measured by the covering number, which can have dimension-independent cardinality in…

Machine Learning · Statistics 2022-09-20 Sejun Park , Umut Şimşekli , Murat A. Erdogdu

We present a complete study of boundary bound states and related boundary S-matrices for the sine-Gordon model with Dirichlet boundary conditions. Our approach is based partly on the bootstrap procedure, and partly on the explicit solution…

High Energy Physics - Theory · Physics 2016-09-06 S. Skorik , H. Saleur

In this paper, we study the numerical approximation of a general second order semilinear stochastic partial differential equation (SPDE) driven by a additive fractional Brownian motion (fBm) with Hurst parameter $H>\frac 12$ and Poisson…

Numerical Analysis · Mathematics 2020-01-01 Aurelien Junior Noupelah , Antoine Tambue

Orthogonality constrained optimization is widely used in applications from science and engineering. Due to the nonconvex orthogonality constraints, many numerical algorithms often can hardly achieve the global optimality. We aim at…

Optimization and Control · Mathematics 2019-06-18 Honglin Yuan , Xiaoyi Gu , Rongjie Lai , Zaiwen Wen

This paper addresses the robust stability of a boundary controlled system coupling two partial differential equations (PDEs), namely beam and string equations, in the presence of boundary and in-domain disturbances under the framework of…

Analysis of PDEs · Mathematics 2018-11-19 Jun Zheng , Hugo Lhachemi , Guchuan Zhu , David Saussi

Surface integral equations (SIEs)-based boundary element methods are widely used for analyzing electromagnetic scattering scenarii. However, after discretization of SIEs, the spectrum and eigenvectors of the boundary element matrices are…

Numerical Analysis · Mathematics 2025-05-19 Rui Chen , Adrien Merlini , Francesco P. Andriulli

Source localization based on electroencephalography (EEG) has become a widely used neuroimagining technique. However its precision has been shown to be very dependent on how accurately the brain, head and scalp can be electrically modeled…

Medical Physics · Physics 2020-04-22 Lyes Rahmouni , Adrien Merlini , Axelle Pillain , Francesco P. Andriulli

This work focuses on model preparation for electrostatic simulations of CAD designs to realize a rapid virtual prototyping concept. We present a boundary element method (BEM) allowing discontinuous fields between surfaces. The corresponding…

Computational Engineering, Finance, and Science · Computer Science 2024-12-13 Benjamin Marussig , Thomas Rüberg , Jürgen Zechner , Lars Kielhorn , Thomas-Peter Fries

For most finite element simulations, boundary-conforming meshes have significant advantages in terms of accuracy or efficiency. This is particularly true for complex domains. However, with increased complexity of the domain, generating a…

Numerical Analysis · Mathematics 2021-04-07 Jan Helmig , Fabian Key , Marek Behr , Stefanie Elgeti

Neural Ordinary Differential Equations (N-ODEs) are a powerful building block for learning systems, which extend residual networks to a continuous-time dynamical system. We propose a Bayesian version of N-ODEs that enables well-calibrated…

Machine Learning · Computer Science 2020-02-19 Andreas Look , Melih Kandemir

In this paper, we propose a regularized Newton method for computing ground states of Bose-Einstein condensates (BECs), which can be formulated as an energy minimization problem with a spherical constraint. The energy functional and…

Numerical Analysis · Mathematics 2017-11-21 Xinming Wu , Zaiwen Wen , Weizhu Bao

This paper advances the theory and practice of Domain Generalization (DG) in machine learning. We consider the typical DG setting where the hypothesis is composed of a representation mapping followed by a labeling function. Within this…

Machine Learning · Computer Science 2023-05-23 Boyang Lyu , Thuan Nguyen , Prakash Ishwar , Matthias Scheutz , Shuchin Aeron

This paper presents a theoretical discussion as well as novel solution algorithms for problems of scattering on smooth two-dimensional domains under Zaremba boundary conditions for which Dirichlet and Neumann conditions are specified on…

Analysis of PDEs · Mathematics 2015-08-17 Eldar Akhmetgaliyev , Oscar Bruno

We present a novel moving immersed boundary method (IBM) and employ it in direct numerical simulations (DNS) of the closed-vessel swirling von Karman flow in laminar and turbulent regimes. The IBM extends direct-forcing approaches by…

Fluid Dynamics · Physics 2022-02-17 M. Houssem Kasbaoui , Tejas Kulkarni , Fabrizio Bisetti

Many scientific problems involve data that is embedded in a space with periodic boundary conditions. This can for instance be related to an inherent cyclic or rotational symmetry in the data or a spatially extended periodicity. When…

Machine Learning · Computer Science 2025-10-08 Xander M. de Wit , Alessandro Gabbana

Magnetostatic field calculations in micromagnetic simulations can be numerically expensive, particularly in the case of large-scale finite element simulations. The established finite element / boundary element method (FEM/BEM) by Fredkin &…

Numerical Analysis · Mathematics 2019-03-27 Riccardo Hertel , Sven Christophersen , Steffen Börm

This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the…

Numerical Analysis · Mathematics 2020-11-19 Jean Daniel Mukam , Antoine Tambue

Stochastic differential equations (SDEs) offer powerful and accessible mathematical models for capturing both deterministic and probabilistic aspects of dynamic behavior across a wide range of physical, financial, and social systems.…

Statistics Theory · Mathematics 2026-02-17 Paromita Banerjee , Anirban Mondal
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