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Takens' Embedding Theorem asserts that when the states of a hidden dynamical system are confined to a low-dimensional attractor, complete information about the states can be preserved in the observed time-series output through the delay…

Dynamical Systems · Mathematics 2014-06-17 Han Lun Yap , Armin Eftekhari , Michael B. Wakin , Christopher J. Rozell

In this paper, we study averaging principles for a class of time-inhomogeneous stochastic differential equations (SDEs) with slow and fast time-scales, where the drift term in the fast component is time-dependent and only partially…

Probability · Mathematics 2025-06-24 Xiaobin Sun , Jian Wang , Yingchao Xie

The Eulerian variational formulation is presented to obtain governing equations of the electromagnetic turbulent gyrokinetic system. A local momentum balance in the system is derived from the invariance of the Lagrangian of the system under…

Plasma Physics · Physics 2024-04-09 H. Sugama

The aim of this paper is to introduce a finite element formulation within Arbitrary Lagrangian Eulerian framework with vanishing discrete {\it Space Conservation Law} (SCL) for differential equations on time dependent domains. The novelty…

Numerical Analysis · Mathematics 2018-09-19 Filip Ivancic , Tony W. -H. Sheu , Maxim Solovchuk

We consider mixing problems in the form of transient convection--diffusion equations with a velocity vector field with multiscale character and rough data. We assume that the velocity field has two scales, a coarse scale with slow spatial…

Numerical Analysis · Mathematics 2014-05-05 Erik Burman

In this paper, we present a thorough von Neumann stability analysis of explicit and implicit Arbitrary-Lagrangian-Eulerian (ALE) ADER discontinuous Galerkin (DG) methods on classical and degenerate spacetime geometries for hyperbolic…

Numerical Analysis · Mathematics 2026-02-11 Mauro Bonafini , Davide Torlo , Elena Gaburro

The Fractional Langevin Equation (FLE) describes a non-Markovian Generalized Brownian Motion with long time persistence (superdiffusion), or anti-persistence (subdiffusion) of both velocity-velocity correlations, and position increments. It…

Statistical Mechanics · Physics 2011-07-11 P. Siegle , I. Goychuk , P. Hanggi

Discrete Element Methods (DEM), i.e.~the simulation of many rigid particles, suffer from very stiff differential equations plus multiscale challenges in space and time. The particles move smoothly through space until they interact almost…

Mathematical Software · Computer Science 2023-09-28 Peter Noble , Tobias Weinzierl

The dispersion of solute in porous media shows a non-linear increase in the transition from diffusion to advection dominated dispersion as the flow velocity is raised. In the past, the behavior in this intermediate regime has been explained…

Fluid Dynamics · Physics 2014-09-23 B. Ph. van Milligen , P. D. Bons

Semi-discrete Galerkin formulations of transient wave equations, either with conforming or discontinuous Galerkin finite element discretizations, typically lead to large systems of ordinary differential equations. When explicit time…

Numerical Analysis · Mathematics 2012-10-19 Marcus Grote , Teodora Mitkova

Using a Lagrangian framework, we discuss different conceptualizations of the blockscale dispersion tensor. We distinguish effective and ensemble blockscale dispersion, which measure the impact of subscale velocity fluctuations on solute…

Fluid Dynamics · Physics 2016-11-28 Felipe P. J. de Barros , Marco Dentz

Defect transport is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no measure of convergence and require significant…

Computational Physics · Physics 2020-09-04 Thomas D Swinburne , Danny Perez

When a fluid carrying a passive solute flows quickly through porous media, three key macroscale transport mechanisms occur. These mechanisms are diffusion, advection and dispersion, all of which depend on the microstructure of the porous…

Fluid Dynamics · Physics 2024-10-16 Lucy C Auton , Mohit P. Dalwadi , Ian M. Griffiths

An approach that extends equilibrium thermodynamics principles to out-of-equilibrium systems is based on the local equilibrium hypothesis. However, the validity of the a priori assumption of local equilibrium has been questioned due to the…

Fluid Dynamics · Physics 2022-02-04 Atanu Chatterjee , Takahiko Ban , Germano Iannacchione

We consider the problem of inferring latent stochastic differential equations (SDEs) with a time and memory cost that scales independently with the amount of data, the total length of the time series, and the stiffness of the approximate…

Machine Learning · Computer Science 2023-12-19 Kevin Course , Prasanth B. Nair

We study the global and the local transport and diffusion in the case of the standard map, by calculating the diffusion exponent $\mu$. In the global case we find that the mean diffusion exponent for the whole phase space is either $\mu=1$,…

Chaotic Dynamics · Physics 2018-07-25 Mirella Harsoula , George Contopoulos

Spatiotemporal forecasting in physical systems, such as large-scale traffic networks, requires modeling a dual dynamic: continuous macroscopic rhythms and discrete, unpredictable microscopic shocks. While Neural Ordinary Differential…

Machine Learning · Computer Science 2026-05-06 Xiao Zhang , Yafei Li , Ruixiang Wang , Wei Wei , Shuo He , Mingliang Xu

The coupling between advection and diffusion in position space can often lead to enhanced mass transport compared to diffusion without flow. An important framework used to characterize the long-time diffusive transport in position space is…

Fluid Dynamics · Physics 2024-10-10 Zhiwei Peng

Accurately predicting spatio-temporal network traffic is essential for dynamically managing computing resources in modern communication systems and minimizing energy consumption. Although spatio-temporal traffic prediction has received…

Machine Learning · Computer Science 2026-03-24 Xintong Wang , Haihan Nan , Ruidong Li , Huaming Wu

Systems governed by partial differential equations (PDEs) require computationally intensive numerical solvers to predict spatiotemporal field evolution. While machine learning (ML) surrogates offer faster solutions, autoregressive inference…

Machine Learning · Computer Science 2025-07-08 Ishan Khurjekar , Indrashish Saha , Lori Graham-Brady , Somdatta Goswami
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