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This paper describes an interdisciplinary approach to geometry modeling of geospatial boundaries. The objective is to extract surfaces from irregular spatial patterns using differential geometry and obtain coherent directional predictions…

Computational Engineering, Finance, and Science · Computer Science 2020-06-09 Raymond Leung

This paper introduces the generalized forward-backward splitting algorithm for minimizing convex functions of the form $F + \sum_{i=1}^n G_i$, where $F$ has a Lipschitz-continuous gradient and the $G_i$'s are simple in the sense that their…

Optimization and Control · Mathematics 2014-02-11 Hugo Raguet , Jalal Fadili , Gabriel Peyré

High-order partial differential equations (PDEs) require derivative regularity that standard $C^0$ finite element infrastructures do not directly provide on unstructured meshes. We propose a mesh-intrinsic generalized finite element method…

Numerical Analysis · Mathematics 2026-04-28 Rong Tian

We propose a multiscale spectral generalized finite element method (MS-GFEM) for discontinuous Galerkin (DG) discretizations. The method builds local approximations on overlapping subdomains as the sum of a local source solution and a…

Numerical Analysis · Mathematics 2026-01-15 Christian Alber , Lukas Holbach

We present a general framework, treating Lipschitz domains in Riemannian manifolds, that provides conditions guaranteeing the existence of norming sets and generalized local polynomial reproduction - a powerful tool used in the analysis of…

Classical Analysis and ODEs · Mathematics 2025-11-11 Thomas Hangelbroek , Christian Rieger , Grady B. Wright

Neural network-based methods are effective for solving equilibria in Mean-Field Games (MFGs), particularly in high-dimensional settings. However, solving the coupled partial differential equations (PDEs) in MFGs limits their applicability…

Machine Learning · Computer Science 2025-04-07 Jinwei Liu , Wang Yao , Xiao Zhang

We present a novel integral-equation algorithm for evaluation of Zaremba eigenvalues and eigenfunctions}, that is, eigenvalues and eigenfunctions of the Laplace operator with mixed Dirichlet-Neumann boundary conditions; of course, (slight…

Numerical Analysis · Mathematics 2016-05-04 Eldar Akhmetgaliyev , Oscar Bruno , Nilima Nigam

We develop a stabilized cut discontinuous Galerkin framework for the numerical solution of el- liptic boundary value and interface problems on complicated domains. The domain of interest is embedded in a structured, unfitted background mesh…

Numerical Analysis · Mathematics 2019-03-27 Ceren Gürkan , André Massing

We introduce a general framework for approximating parabolic Stochastic Partial Differential Equations (SPDEs) based on fluctuation-dissipation balance. Using this approach we formulate Stochastic Discontinuous Galerkin Methods (SDGM). We…

Numerical Analysis · Mathematics 2023-02-28 Will Pazner , Nathaniel Trask , Paul J. Atzberger

We use a diffuse interface method for solving Poisson's equation with a Dirichlet condition on an embedded curved interface. The resulting diffuse interface problem is identified as a standard Dirichlet problem on approximating regular…

Numerical Analysis · Mathematics 2015-11-23 Matthias Schlottbom

Given i.i.d. observations uniformly distributed on a closed submanifold of the Euclidean space, we study higher-order generalizations of graph Laplacians, so-called Hodge Laplacians on graphs, as approximations of the Laplace-Beltrami…

Statistics Theory · Mathematics 2025-04-07 Jan-Paul Lerch , Martin Wahl

We present a direct Poisson solver for massively parallel simulations on three-dimensional Cartesian grids with non-uniform spacing. The method uses a tensor-based formulation in which the operator is diagonalized numerically along two…

Computational Physics · Physics 2026-03-11 Pedro Costa , Duarte Palancha , Joshua Romero , Roberto Verzicco , Massimiliano Fatica

High-dimensional PDEs have been a longstanding computational challenge. We propose to solve high-dimensional PDEs by approximating the solution with a deep neural network which is trained to satisfy the differential operator, initial…

Mathematical Finance · Quantitative Finance 2018-10-17 Justin Sirignano , Konstantinos Spiliopoulos

In this paper we present and analyse a discontinuous Galerkin finite element method (DGFEM) for the approximation of solutions to elliptic partial differential equations in nondivergence form, with oblique boundary conditions, on curved…

Numerical Analysis · Mathematics 2018-10-01 Ellya Kawecki

Given an orthogonal lattice with mesh length h on a bounded convex domain, we propose to approximate the Aleksandrov solution of the Monge-Ampere equation by regularizing the data and discretizing the equation in a subdomain using the…

Numerical Analysis · Mathematics 2015-07-31 Gerard Awanou

A new and efficient neural-network and finite-difference hybrid method is developed for solving Poisson equation in a regular domain with jump discontinuities on embedded irregular interfaces. Since the solution has low regularity across…

Numerical Analysis · Mathematics 2023-06-13 Wei-Fan Hu , Te-Sheng Lin , Yu-Hau Tseng , Ming-Chih Lai

We prove a Hardy inequality for uniformly elliptic operators subject to Dirichlet or mixed boundary conditions on domains $\Omega$ with piecewiese smooth boundary in arbitrary Riemannian Manifolds (M, g). Employing an approach of E.B.…

Spectral Theory · Mathematics 2014-01-22 Nils Rautenberg

Solving partial differential equations (PDEs) on complex domains can present significant computational challenges. The Diffuse Domain Method (DDM) is an alternative that reformulates the partial differential equations on a larger, simpler…

Numerical Analysis · Mathematics 2025-07-24 Luke Benfield , Andreas Dedner

In this paper we present a novel approach for the design of high order general boundary conditions when approximating solutions of the Euler equations on domains with curved boundaries, using meshes which may not be boundary conformal. When…

Numerical Analysis · Mathematics 2023-12-13 Mirco Ciallella , Stephane Clain , Elena Gaburro , Mario Ricchiuto

We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold geodesic distance between any pair of points using both the…

Machine Learning · Statistics 2019-09-16 Amit Moscovich , Ariel Jaffe , Boaz Nadler