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We introduce a dimensional splitting method based on the intertwining property of the Radon transform, with a particular focus on its applications related to hyperbolic partial differential equations (PDEs). This dimensional splitting has…

Numerical Analysis · Mathematics 2018-12-27 Donsub Rim

Stochastic differential equations (SDEs) are established tools to model physical phenomena whose dynamics are affected by random noise. By estimating parameters of an SDE intrinsic randomness of a system around its drift can be identified…

Computation · Statistics 2012-05-03 Umberto Picchini , Susanne Ditlevsen

Presenting systems of differential equations in the form of diagrams has become common in certain parts of physics, especially electromagnetism and computational physics. In this work, we aim to put such use of diagrams on a firm…

Mathematical Physics · Physics 2022-06-20 Evan Patterson , Andrew Baas , Timothy Hosgood , James Fairbanks

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here…

In this paper, a novel dual-field structure-preserving mixed finite element discretization for incompressible Hall MHD equations is introduced. The discretization satisfies pointwise conservation of mass, magnetic Gauss's law, and…

Numerical Analysis · Mathematics 2026-05-20 Yi Zhang

We introduce suitable coordinate systems for pipes and their variants that allow us to transform partial differential equations (PDEs) on the pipe surfaces or in the solid pipes into computational domains with fixed limits/ranges. Such a…

Numerical Analysis · Mathematics 2025-09-09 Shuaifei Hu , Yujian Jiao , Desong Kong , Li-Lian Wang

We construct a sheaf theoretic and derived geometric machinery to study nonlinear partial differential equations and their singular supports. We establish a notion of derived microlocalization for solution spaces of non-linear equations and…

Algebraic Geometry · Mathematics 2024-06-18 Jacob Kryczka , Artan Sheshmani , Shing-Tung Yau

As Physics did in previous centuries, there is currently a common dream of extracting generic laws of nature in economics, sociology, neuroscience, by focalising the description of phenomena to a minimal set of variables and parameters,…

Physics and Society · Physics 2016-10-14 Fatihcan M. Atay , Sven Banisch , Philippe Blanchard , Bruno Cessac , Eckehard Olbrich

The study of bifurcations of differential-algebraic equations (DAEs) is the topic of interest for many applied sciences, such as electrical engineering, robotics, etc. While some of them were investigated already, the full classification of…

Dynamical Systems · Mathematics 2022-01-13 Ivan Ovsyannikov , Haibo Ruan

Partial differential equations (PDEs) with multiple scales or those defined over sufficiently large domains arise in various areas of science and engineering and often present problems when approximating the solutions numerically. Machine…

Numerical Analysis · Mathematics 2024-05-27 Eddel Elí Ojeda Avilés , Daniel Olmos-Liceaga , Jae-Hun Jung

The Lie-point symmetry method is used to find some closed-form solutions for a constitutive equation modeling stress in elastic materials. The partial differential equation (PDE), which involves a power law with arbitrary exponent n, was…

Exactly Solvable and Integrable Systems · Physics 2024-12-17 Rehana Naz , Willy Hereman

Linear systems with large differences between coefficients ("discontinuous coefficients") arise in many cases in which partial differential equations(PDEs) model physical phenomena involving heterogeneous media. The standard approach to…

Mathematical Software · Computer Science 2009-05-04 Dan Gordon , Rachel Gordon

Recently, researchers have utilized neural networks to accurately solve partial differential equations (PDEs), enabling the mesh-free method for scientific computation. Unfortunately, the network performance drops when encountering a high…

Machine Learning · Computer Science 2021-09-29 Pongpisit Thanasutives , Masayuki Numao , Ken-ichi Fukui

Enhancing neural networks with knowledge of physical equations has become an efficient way of solving various physics problems, from fluid flow to electromagnetism. Graph neural networks show promise in accurately representing irregularly…

Machine Learning · Computer Science 2022-04-01 Mike Y. Michelis , Robert K. Katzschmann

In this paper, we study some relationships existing between some particular mathematical structures: discrete surfaces coming from discrete topology and mathematical morphology, poset-based connected manifolds coming from discrete topology,…

Algebraic Topology · Mathematics 2025-08-05 Nicolas Boutry

Continuum equations are ubiquitous in physical modelling of elastic, viscous, and viscoelastic systems. The equations of continuum mechanics take nontrivial forms on curved surfaces. Although the curved surface formulation of the continuum…

Classical Physics · Physics 2024-07-30 Sujit Kumar Nath

Piezoelectric appliances have become hugely important in the past century and computer simulations play an essential part in the modern design process thereof. While much work has been invested into the practical simulation of piezoelectric…

Analysis of PDEs · Mathematics 2019-07-11 Benjamin Jurgelucks , Tom Lahmer , Veronika Schulze

A multi-cube method is developed for solving systems of elliptic and hyperbolic partial differential equations numerically on manifolds with arbitrary spatial topologies. It is shown that any three-dimensional manifold can be represented as…

Computational Physics · Physics 2015-06-11 Lee Lindblom , Bela Szilagyi

Applications in quantitative finance such as optimal trade execution, risk management of options, and optimal asset allocation involve the solution of high dimensional and nonlinear Partial Differential Equations (PDEs). The connection…

Machine Learning · Statistics 2019-10-28 Batuhan Güler , Alexis Laignelet , Panos Parpas

Model discrepancy, defined as the difference between model predictions and reality, is ubiquitous in computational models for physical systems. It is common to derive partial differential equations (PDEs) from first principles physics, but…

Numerical Analysis · Mathematics 2022-11-08 Joseph Hart , Bart van Bloemen Waanders
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