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Given an autonomous system of ordinary differential equations (ODE), we consider developing practical models for the deterministic, slow/coarse behavior of the ODE system. Two types of coarse variables are considered. The first type…

Dynamical Systems · Mathematics 2015-06-05 Likun Tan , Amit Acharya , Kaushik Dayal

We consider slow-fast systems of differential equations, in which both the slow and fast variables are perturbed by noise. When the deterministic system admits a uniformly asymptotically stable slow manifold, we show that the sample paths…

Probability · Mathematics 2007-05-23 Nils Berglund , Barbara Gentz

We consider systems characterized by the presence of a rapidly oscillating force. A general method is presented for the construction of the effective action governing the large-scale nonlinear dynamics of such systems order by order in…

Chaotic Dynamics · Physics 2026-05-29 Afshin Besharat , Alexander A. Penin

Attosecond physics provides unique insights into light-matter interaction on ultrafast time scales. Its core phenomenon, High Harmonic Generation (HHG), is often described by a classical recollision model, the simple-man or three-step…

Classical Physics · Physics 2024-05-14 Jonathan Berkheim , David J. Tannor

Recently, a surrogate model was proposed that employs a factorization machine to approximate the underlying input-output mapping of the original system, with quantum annealing used to optimize the resulting surrogate function. Inspired by…

Machine Learning · Computer Science 2026-01-21 Anbang Wang , Dunbo Cai , Yu Zhang , Yangqing Huang , Xiangyang Feng , Zhihong Zhang

This work is inspired by a recent study of a two-dimensional stochastic fragmentation model. We show that the configurational entropy of this model exhibits log-periodic oscillations as a function of the sample size, by exploiting an exact…

Statistical Mechanics · Physics 2024-05-24 Jean-Marc Luck

The estimation of unknown values of parameters (or hidden variables, control variables) that characterise a physical system often relies on the comparison of measured data with synthetic data produced by some numerical simulator of the…

Machine Learning · Computer Science 2019-01-28 Xi Chen , Mike Hobson

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly non-trivial statistical behavior, motivating the development of surrogate models…

The paper is concerned with defining the electrical signals and their models. The delays are discussed, the asynchronous automata - which are the models of the asynchronous circuits - and the examples of the clock generator and of the R-S…

Logic in Computer Science · Computer Science 2007-05-23 Serban E. Vlad

Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e.g., marketing and medicine. Despite its success in certain domains, most existing methods…

Machine Learning · Computer Science 2023-11-22 Ruichu Cai , Weilin Chen , Zeqin Yang , Shu Wan , Chen Zheng , Xiaoqing Yang , Jiecheng Guo

Synchronization in a population of oscillators with hyperbolic chaotic phases is studied for two models. One is based on the Kuramoto dynamics of the phase oscillators and on the Bernoulli map applied to these phases. This system possesses…

Chaotic Dynamics · Physics 2020-11-24 Arkady Pikovsky

We examine the use of synchronization as a mechanism for extracting parameter and state information from experimental systems. We focus on important aspects of this problem that have received little attention previously, and we explore them…

The paper revisits the $\alpha$--regression framework for compositional data. The model uses a flexible power transformation parameterized by $\alpha$ to interpolate between raw data analysis and log--ratio methods, naturally handling zeros…

Methodology · Statistics 2026-05-14 Michail Tsagris , Yannis Pantazis

The long-time behavior of transport coefficients in a model for spatially heterogeneous media in two and three dimensions is investigated by Molecular Dynamics simulations. The behavior of the velocity auto-correlation function is…

Soft Condensed Matter · Physics 2007-05-23 Felix Höfling , Thomas Franosch

Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turbine. Stochasticity in the inflow turbulence and irregular…

Applications · Statistics 2022-06-15 Deepali Singh , Richard P. Dwight , Kasper Laugesen , Laurent Beaudet , Axelle Viré

This paper presents an application of partial contraction analysis to the study of global synchronization in discrete chaotic systems. Explicit sufficient conditions on the coupling strength of networks of discrete oscillators are derived.…

Chaotic Dynamics · Physics 2007-05-23 Juan C. Botero , Jean-Jacques E. Slotine

High-speed flight vehicles, which travel much faster than the speed of sound, are crucial for national defense and space exploration. However, accurately predicting their behavior under numerous, varied flight conditions is a challenge and…

Machine Learning · Computer Science 2024-11-07 Tyler E. Korenyi-Both , Nathan J. Falkiewicz , Matthew C. Jones

We investigate a spatially discrete surrogate model for the dynamics of a slender, elastic, inextensible fiber in turbulent flows. Deduced from a continuous space-time beam model for which no solution theory is available, it consists of a…

Probability · Mathematics 2017-04-28 Felix Lindner , Nicole Marheineke , Holger Stroot , Alexander Vibe , Raimund Wegener

A synchronization mechanism driven by annealed noise is studied for two replicas of a coupled-map lattice which exhibits stable chaos (SC), i.e. irregular behavior despite a negative Lyapunov spectrum. We show that the observed…

Statistical Mechanics · Physics 2007-05-23 F. Bagnoli , F. Cecconi

Autoregressive surrogate models (or \textit{emulators}) of spatiotemporal systems provide an avenue for fast, approximate predictions, with broad applications across science and engineering. At inference time, however, these models are…

Machine Learning · Computer Science 2025-07-10 Chris Pedersen , Laure Zanna , Joan Bruna