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Diffusion models (DMs) have been adopted across diverse fields with its remarkable abilities in capturing intricate data distributions. In this paper, we propose a Fast Diffusion Model (FDM) to significantly speed up DMs from a stochastic…

Computer Vision and Pattern Recognition · Computer Science 2023-10-05 Zike Wu , Pan Zhou , Kenji Kawaguchi , Hanwang Zhang

We study experimentally the thermal fluctuations of energy input and dissipation in a harmonic oscillator driven out of equilibrium, and search for Fluctuation Relations. We study transient evolution from the equilibrium state, together…

Statistical Mechanics · Physics 2007-11-28 Sylvain Joubaud , Nicolas Garnier , Sergio Ciliberto

In this Letter, we present a new experimental approach to investigate the effective temperature concept as a generalization of the fluctuation-dissipation theorem (FDT) for nonequilibrium systems. Simultaneous measurements of diffusion…

Soft Condensed Matter · Physics 2017-12-06 Jean Colombani , Laure Petit , Christophe Ybert , Catherine Barentin

We study the non-equilibrium version of the fluctuation-dissipation theorem (FDT) within the glass phase of Bouchaud's trap model. We incorporate into the model an arbitrary observable m and obtain its correlation and response functions in…

Statistical Mechanics · Physics 2009-11-07 Suzanne Fielding , Peter Sollich

Dropout is a widely utilized regularization technique in the training of neural networks, nevertheless, its underlying mechanism and its impact on achieving good generalization abilities remain poorly understood. In this work, we derive the…

Machine Learning · Computer Science 2023-05-26 Zhongwang Zhang , Yuqing Li , Tao Luo , Zhi-Qin John Xu

A unified derivation of the off equilibrium fluctuation dissipation relations (FDR) is given for Ising and continous spins to arbitrary order, within the framework of Markovian stochastic dynamics. Knowledge of the FDR allows to develop…

Statistical Mechanics · Physics 2009-06-15 Eugenio Lippiello , Federico Corberi , Alessandro Sarracino , Marco Zannetti

Based on the covariant underdamped and overdamped Langevin equations with Stratonovich coupling to multiplicative noises and the associated Fokker-Planck equations on Riemannian manifold, we present the first law of stochastic…

Statistical Mechanics · Physics 2024-04-02 Yifan Cai , Tao Wang , Liu Zhao

Brownian yet non-Gaussian processes have recently been observed in numerous biological systems and the corresponding theories have been built based on random diffusivity models. Considering the particularity of random diffusivity, this…

Statistical Mechanics · Physics 2022-08-31 Xudong Wang , Yao Chen

The violation of the Fluctuation-Dissipation Theorem (FDT) in a two-dimensional Ising model with both ferromagnetic exchange and antiferromagnetic dipolar interactions is established and investigated via Monte Carlo simulations. Through the…

Disordered Systems and Neural Networks · Physics 2009-10-31 Daniel A. Stariolo , Sergio A. Cannas

In this Article we review some recent progresses in the field of non-equilibrium linear response theory. We show how a generalization of the fluctuation-dissipation theorem can be derived for Markov processes, and discuss the…

Statistical Mechanics · Physics 2007-07-06 Federico Corberi , Eugenio Lippiello , Marco Zannetti

Near equilibrium, where all currents of a system vanish on average, the fluctuation-dissipation relation (FDR) connects a current's spontaneous fluctuations with its response to perturbations of the conjugate thermodynamic force. Out of…

Statistical Mechanics · Physics 2016-11-02 Bernhard Altaner , Matteo Polettini , Massimiliano Esposito

We establish a novel generalization of the fluctuation theorem for partially-masked nonequilibrium dynamics. We introduce a partial entropy production with a subset of all possible transitions, and show that the partial entropy production…

Statistical Mechanics · Physics 2015-01-20 Naoto Shiraishi , Takahiro Sagawa

Perturbed Einstein's equations with a linear response relation and a stochastic source, applicable to a relativistic star model are worked out . These perturbations which are stochastic in nature, are of significance for building a…

General Relativity and Quantum Cosmology · Physics 2023-02-22 Seema Satin

In the attempt to derive the regression theorem from the fluctuation dissipation theorem several authors claim the violation of the former theorem in the quantum case. Here we pose the question: does it exists a quantum fluctuation…

Statistical Mechanics · Physics 2009-11-07 P. Shiktorov , E. Starikov , V. Gruzinskis , L. Reggiani

Numerical simulations of various domain growth systems are reported, in order to compute the parameter describing the violation of fluctuation dissipation theorem (FDT) in aging phenomena. We compute two-times correlation and response…

Condensed Matter · Physics 2009-10-30 A. Barrat

To integrate hydrodynamic fluctuations, namely thermal fluctuations of hydrodynamics, into dynamical models of high-energy nuclear collisions based on relativistic hydrodynamics, the property of the hydrodynamic fluctuations given by the…

Nuclear Theory · Physics 2019-10-29 Koichi Murase

The mean field (MF) theory of multilayer neural networks centers around a particular infinite-width scaling, where the learning dynamics is closely tracked by the MF limit. A random fluctuation around this infinite-width limit is expected…

Machine Learning · Computer Science 2021-11-01 Huy Tuan Pham , Phan-Minh Nguyen

Fluctuations arising in nonlinear dissipative systems (diode, transistors, chemical reaction, etc.) subject to an external drive (voltage, chemical potential, etc.) are well known to elude any simple characterization such as the…

Statistical Mechanics · Physics 2023-07-10 Jean-Charles Delvenne , Léopold Van Brandt

A dissipation theory is proposed for the potentials-based FDTD algorithm for the case of inhomogeneous lossless media. We show that under the Courant-Friedrichs-Lewy (CFL) limit, the equations describing the time evolution of scalar and…

Numerical Analysis · Mathematics 2022-03-21 Fadime Bekmambetova , Piero Triverio

The algorithms used to train neural networks, like stochastic gradient descent (SGD), have close parallels to natural processes that navigate a high-dimensional parameter space -- for example protein folding or evolution. Our study uses a…

Machine Learning · Computer Science 2023-06-07 Shishir Adhikari , Alkan Kabakçıoğlu , Alexander Strang , Deniz Yuret , Michael Hinczewski