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The numerical simulation of fluid flow through a complex geometry with heat transfer is of strong interest for many applications, such as oil-filled power transformers. A fundamental challenge here is that high resolution is necessary to…

Computational Physics · Physics 2020-10-19 Ole H. H. Meyer , Karl Yngve Lervåg , Åsmund Ervik

Trefftz schemes are high-order Galerkin methods whose discrete spaces are made of elementwise exact solutions of the underlying PDE. Trefftz basis functions can be easily computed for many PDEs that are linear, homogeneous, and have…

Numerical Analysis · Mathematics 2026-03-10 Lise-Marie Imbert-Gérard , Andrea Moiola , Chiara Perinati , Paul Stocker

Efficient computation of graph diffusion equations (GDEs), such as Personalized PageRank, Katz centrality, and the Heat kernel, is crucial for clustering, training neural networks, and many other graph-related problems. Standard iterative…

Machine Learning · Computer Science 2024-12-24 Jiahe Bai , Baojian Zhou , Deqing Yang , Yanghua Xiao

Numerical analytical heat transfer models play a critical role in geothermal design and feasibility studies. Classical solutions, such as those proposed by Gringarten et al. 1975, rely on simplified assumptions and systematically…

Computation of electromagnetic fields due to point sources (Hertzian dipoles) in cylindrically stratified media is a classical problem for which analytical expressions of the associated tensor Green's function have been long known. However,…

Mathematical Physics · Physics 2014-05-30 H. Moon , F. L. Teixeira , B. Donderici

In this paper, we derive closed form solutions for the quasi-stationary problems of moving heat sources within the gradient theory of heat transfer. This theory can be formally deduced from the two-temperature model and it can be treated as…

Other Condensed Matter · Physics 2022-02-15 Y. O. Solyaev , S. A. Lurie

We consider a diffusion process on an evolving surface with a piecewise Lipschitz-continuous boundary from an energetic point of view. We employ an energetic variational approach with both surface divergence and transport theorems to derive…

Mathematical Physics · Physics 2018-10-19 Hajime Koba

The aim of this paper is to provide a mathematical analysis of transformer architectures using a self-attention mechanism with layer normalization. In particular, observed patterns in such architectures resembling either clusters or uniform…

Analysis of PDEs · Mathematics 2025-04-29 Martin Burger , Samira Kabri , Yury Korolev , Tim Roith , Lukas Weigand

Generalized Hydrodynamics (GHD) has recently been devised as a method to solve the dynamics of integrable quantum many-body systems beyond the mean-field approximation. In its original form, a major limitation is the inability to predict…

This paper exploits the theory of geometric gradient flows to introduce an alternative regularization of the thin-film equation. The solution properties of this regularization are investigated via a sequence of numerical simulations whose…

Fluid Dynamics · Physics 2020-02-20 Darryl D. Holm , Lennon Ó Náraigh , Cesare Tronci

This article presents a theoretical analysis of a one-dimensional heat transfer problem in two layers involving diffusion, advection, internal heat generation or loss linearly dependent on temperature in each layer, and heat generation due…

Fluid Dynamics · Physics 2024-10-28 Guillermo Federico Umbricht , Diana Rubio , Domingo Alberto Tarzia

Convection-diffusion equations provide the basis for describing heat and mass transfer phenomena as well as processes of continuum mechanics. To handle flows in porous media, the fundamental issue is to model correctly the convective…

Numerical Analysis · Computer Science 2012-08-29 A. Churbanov , P. Vabishchevich

"Generalized Hydrodynamics" (GHD) stands for a model that describes one-dimensional \textit{integrable} systems in quantum physics, such as ultra-cold atoms or spin chains. Mathematically, GHD corresponds to nonlinear equations of kinetic…

Computational Physics · Physics 2023-11-21 Frederik Møller , Nicolas Besse , Igor E. Mazets , Hans-Peter Stimming , Norbert J. Mauser

Graph convolution is the core of most Graph Neural Networks (GNNs) and usually approximated by message passing between direct (one-hop) neighbors. In this work, we remove the restriction of using only the direct neighbors by introducing a…

Social and Information Networks · Computer Science 2022-04-06 Johannes Gasteiger , Stefan Weißenberger , Stephan Günnemann

The analogy to heat diffusion has enhanced our understanding of information flow in graphs and inspired the development of Graph Neural Networks (GNNs). However, most diffusion-based GNNs emulate passive heat diffusion, which still suffers…

Machine Learning · Computer Science 2025-10-23 Mengying Jiang

When the variations of surface temperature are measured both spatially and temporally, analytical expressions that correctly account for multi-dimensional transient conduction can be applied. To enhance the accessibility of these accurate…

Instrumentation and Detectors · Physics 2024-12-03 David Buttsworth , Timothy Buttsworth

Continuous graph neural models based on differential equations have expanded the architecture of graph neural networks (GNNs). Due to the connection between graph diffusion and message passing, diffusion-based models have been widely…

Machine Learning · Computer Science 2024-04-01 Kaiyuan Cui , Xinyan Wang , Zicheng Zhang , Weichen Zhao

A construction of a pseudo-differential operator on non-archimedean local fields invariant under a finite group action is given together with the solution of the corresponding Cauchy problem. This construction is applied to parts of the…

Algebraic Geometry · Mathematics 2025-01-07 Patrick Erik Bradley

Normal and anomalous diffusion are ubiquitous in many complex systems [1] . Here, we define a time and space generalized diffusion equation (GDE), which uses fractional-time derivatives and transformed d-path Laplacian operators on…

Physics and Society · Physics 2022-02-02 Fernando Diaz-Diaz , Ernesto Estrada

We study evolution equations on metric graphs with reservoirs, that is graphs where a one-dimensional interval is associated to each edge and, in addition, the vertices are able to store and exchange mass with these intervals. Focusing on…

Analysis of PDEs · Mathematics 2024-12-24 Georg Heinze , Jan-Frederik Pietschmann , André Schlichting
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