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Related papers: Numerical simulations of directed sandpile models

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Deep neural networks (DNNs) exhibit crackling-like avalanches whose origin lacks a mechanistic explanation. Here, I derive a stochastic theory of deep information propagation (DIP) by incorporating Central Limit Theorem (CLT)-level…

Disordered Systems and Neural Networks · Physics 2025-12-02 Arsham Ghavasieh

Universal dimensionless quantities, such as Binder ratios and wrapping probabilities, play an important role in the study of critical phenomena. We study the finite-size scaling behavior of the wrapping probability for the Potts model in…

Statistical Mechanics · Physics 2015-07-14 Hao Hu , Youjin Deng

A dissipative stochastic sandpile model is constructed on one and two dimensional small-world networks with different shortcut densities $\phi$ where $\phi=0$ and $1$ represent a regular lattice and a random network respectively. In the…

Statistical Mechanics · Physics 2017-10-25 Himangsu Bhaumik , S. B. Santra

When simulating multiscale systems, where some fields cannot be fully prescribed despite their effects on the simulation's accuracy, closure models are needed. This phenomenon is observed in turbulent fluid dynamics, where Large Eddy…

Fluid Dynamics · Physics 2025-12-01 Eduardo Vital , Jean-Marc Gratien , Yassine Ayoun , Thibault Faney , Julien Bohbot

When traversing a symmetry breaking second order phase transition at a finite rate, topological defects form whose number dependence on the quench rate is given by simple power laws. We propose a general approach for the derivation of such…

Statistical Mechanics · Physics 2016-03-02 G. Nikoghosyan , R. Nigmatullin , M. B. Plenio

We introduce the sandpile model on multiplex networks with more than one type of edge and investigate its scaling and dynamical behaviors. We find that the introduction of multiplexity does not alter the scaling behavior of avalanche…

Physics and Society · Physics 2012-03-07 Kyu-Min Lee , K. -I. Goh , I. -M. Kim

This paper proposes a specific type of Local Linear Model, the Shuffled Linear Model (SLM), that can be used as a universal approximator. Local operating points are chosen randomly and linear models are used to approximate a function or…

Dynamical Systems · Mathematics 2013-11-20 Laurens Bliek

We present an extension of Willems' Fundamental Lemma to the class of multi-input multi-output discrete-time feedback linearizable nonlinear systems, thus providing a data-based representation of their input-output trajectories. Two sources…

Optimization and Control · Mathematics 2023-03-17 Mohammad Alsalti , Victor G. Lopez , Julian Berberich , Frank Allgöwer , Matthias A. Müller

Prediction of breakdown in disordered solids under external loading in a question of paramount importance. Here we use a fiber bundle model for disordered solids and record the time series of the avalanche sizes and energy bursts. The time…

Statistical Mechanics · Physics 2022-09-14 Diksha , Soumyajyoti Biswas

We consider output trajectory tracking for a class of uncertain nonlinear systems whose internal dynamics may be modelled by infinite-dimensional systems which are bounded-input, bounded-output stable. We describe under which conditions…

Optimization and Control · Mathematics 2019-12-06 Thomas Berger , Marc Puche , Felix Schwenninger

We construct a new family of chance constrained directional models in stochastic data envelopment analysis, generalizing the deterministic directional models and the chance constrained radial models. We prove that chance constrained…

Probability · Mathematics 2024-10-21 Vicente J. Bolos , Rafael Benitez , Vicente Coll-Serrano

The well known Sandpile model of self-organized criticality generates avalanches of all length and time scales, without tuning any parameters. In the original models the external drive is randomly selected. Here we investigate a drive which…

Statistical Mechanics · Physics 2016-12-19 Marco Winkler , Johannes Falk , Wolfgang Kinzel

Data-driven learning is generalized to consider history-dependent multi-fidelity data, while quantifying epistemic uncertainty and disentangling it from data noise (aleatoric uncertainty). This generalization is hierarchical and adapts to…

Machine Learning · Computer Science 2025-07-21 Jiaxiang Yi , Bernardo P. Ferreira , Miguel A. Bessa

Turbulence remains one of the last unresolved problems of classical physics and a major bottleneck to accurate flow prediction in climate, aerospace, and energy systems. Industrial simulations therefore rely on averaged representations of…

Fluid Dynamics · Physics 2026-05-18 Zhuoran Liu , Haochen Wang , Zhuolin Zhao , Heng Xiao

In recent works (BHP), a generalized universality has been proposed, linking phenomena as dissimilar as 2D magnetism and turbulence. To test these ideas, we performed a MC study of the 2D XY-model. We found that the shape of the probability…

Statistical Mechanics · Physics 2008-12-18 G. Palma , T. Meyer , R. Labbé

Uplift modeling requires experimental data, preferably collected in random fashion. This places a logistical and financial burden upon any organisation aspiring such models. Once deployed, uplift models are subject to effects from concept…

Artificial Intelligence · Computer Science 2019-02-04 Jeroen Berrevoets , Wouter Verbeke

Kinetic equations, which explicitly take into account the branching nature of sandpile avalanches, are derived. The dynamics of the sandpile model is described by the generating functions of a branching process. Having used the results…

Condensed Matter · Physics 2009-10-28 E. V. Ivashkevich

We investigate the failure characteristics of complex networks within the framework of the fiber bundle model subject to the local load sharing rule in which the load of the broken fiber is transferred only to its neighbor fibers. Although…

Statistical Mechanics · Physics 2007-05-23 Dong-Hee Kim , Beom Jun Kim , Hawoong Jeong

The self-similar cluster fluctuations of directed bond percolation at the percolation threshold are studied using techniques borrowed from inter\-mit\-ten\-cy-related analysis in multi-particle production. Numerical simulations based on the…

High Energy Physics - Lattice · Physics 2008-11-26 Malte Henkel , Robert Peschanski

In this paper we consider kinetically constrained models (KCM) on $\mathbb Z^2$ with general update families $\mathcal U$. For $\mathcal U$ belonging to the so-called "critical class" our focus is on the divergence of the infection time of…

Probability · Mathematics 2021-12-07 Ivailo Hartarsky , Fabio Martinelli , Cristina Toninelli