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Lagrangian transport structures for three-dimensional and time-dependent fluid flows are of great interest in numerous applications, particularly for geophysical or oceanic flows. In such flows, chaotic transport and mixing can play…

流体动力学 · 物理学 2014-05-09 Rodolphe Chabreyrie , Stefan G. Llewellyn Smith

We report the first analytical calculation of the distribution of the time headways in some special cases of a particle-hopping model of vehicular traffic on idealized single-lane highways and compare with the corresponding results of our…

统计力学 · 物理学 2008-02-03 Kingshuk Ghosh , Arnab Majumdar , Debashish Chowdhury

We address the description of solutes flow with trapping processes in porous media. Starting from a small-scale model for tracer particles trajectories, we derive the corresponding governing equations for the concentration of the mobile and…

统计力学 · 物理学 2010-01-18 Marie-Christine Néel , Andrea Zoia , Maminirina Joelson

In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network…

系统与控制 · 计算机科学 2017-02-08 Michael Chertkov , Sidhant Misra , Marc Vuffray , Dvijotham Krishnamurty , Pascal Van Hentenryck

Rectified flow (Liu et al., 2022; Liu, 2022; Wu et al., 2023) is a method for defining a transport map between two distributions, and enjoys popularity in machine learning, although theoretical results supporting the validity of these…

统计理论 · 数学 2025-12-11 Gonzalo Mena , Arun Kumar Kuchibhotla , Larry Wasserman

Natural phenomena frequently involve a very large number of interacting molecules moving in confined regions of space. Cellular transport by motor proteins is an example of such collective behavior. We derive a deterministic compartmental…

亚细胞过程 · 定量生物学 2017-11-01 Yoram Zarai , Michael Margaliot , Anatoly B. Kolomeisky

Stochastic processes generated by non-stationary distributions are difficult to represent with conventional models such as Gaussian processes. This work presents Recurrent Autoregressive Flows as a method toward general stochastic process…

机器学习 · 计算机科学 2020-06-20 John Mern , Peter Morales , Mykel J. Kochenderfer

This work addresses the problem of learning the dynamics of high-dimensional probability densities over time using unlabeled samples, without assuming access to trajectory information. We introduce two-parameter flows that learn only…

机器学习 · 计算机科学 2026-05-27 Paul Schwerdtner , Tobias Blickhan , Benjamin Peherstorfer

This paper shows how particle hopping models fit into the context of traffic flow theory. Connections between fluid-dynamical traffic flow models, which derive from the Navier-Stokes-equations, and particle hopping models are shown. In some…

凝聚态物理 · 物理学 2009-10-28 Kai Nagel

This paper proposes a crowd dynamic macroscopic model grounded on microscopic phenomenological observations which are upscaled by means of a formal mathematical procedure. The actual applicability of the model to real world problems is…

数学物理 · 物理学 2016-11-07 Luca Bruno , Alessandro Corbetta , Andrea Tosin

This paper addresses the problem of steering an initial probability distribution to a target probability distribution through a deterministic or stochastic linear control system. Our proposed approach is inspired by the flow matching…

最优化与控制 · 数学 2025-01-15 Yuhang Mei , Mohammad Al-Jarrah , Amirhossein Taghvaei , Yongxin Chen

We propose a simple model to analyze the traffic of droplets in microfluidic ``dual networks''. Such functional networks which consist of two types of channels, namely those accessible or forbidden to droplets, often display a complex…

流体动力学 · 物理学 2008-01-30 M. Schindler , A. Ajdari

Complex network approaches have been successfully applied for studying transport processes in complex systems ranging from road, railway or airline infrastructure over industrial manufacturing to fluid dynamics. Here, we utilize a generic…

流体动力学 · 物理学 2017-04-05 Naoya Fujiwara , Kathrin Kirchen , Jonathan F. Donges , Reik V. Donner

It is understood that congestion in traffic can be interpreted in terms of the instability of the equation of dynamic motion. The evolution of a traffic system from an unstable or metastable state to a globally stable state bears a strong…

物理与社会 · 物理学 2016-12-06 Wei-Liang Qian , Bin Wang , Kai Lin , Romuel F. Machado , Yogiro Hama

Consider briefly the equations of fluid dynamics-they describe the enormous wealth of detail in all the interacting physical elements of a fluid flow-whereas in applications we want to deal with a description of just that which is…

chao-dyn · 物理学 2016-08-31 A. J. Roberts

A flow of moving agents can be observed at different scales. Thus, in traffic modeling, three levels are generally considered: the micro, meso and macro levels, representing respectively the interactions between vehicles, groups of vehicles…

多智能体系统 · 计算机科学 2014-01-28 Hassane Abouaïssa , Yoann Kubera , Gildas Morvan

The relation between flow and density is an essential quantitative characteristic to describe the efficiency of traffic systems. We have performed experiments with single-file motion of bicycles and compare the results with previous studies…

Complex systems often have features that can be modeled by advanced mathematical tools [1]. Of special interests are the features of complex systems that have a network structure as such systems are important for modeling technological and…

经典物理 · 物理学 2019-06-13 Nikolay K. Vitanov , Kaloyan N. Vitanov , Zlatinka I. Dimitrova

We have generalized the semi-analytic approach of special flow to the description of flows of passive particles taking into account internal noise. The model is represented by a series of recurrence relations. The recurrence relations are…

统计力学 · 物理学 2023-12-12 Boris S. Maryshev , Lyudmila S. Klimenko

Macroscopic transport modelling aims to predict traffic flows after proposed public policy interventions, such as a new road or railway section or a temporary road closure. As such, it is a vital step in infrastructure planning and…

机器学习 · 计算机科学 2026-05-12 Peter Vanya , Katarína Šimková , Rastislav Farkaš
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