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Predicting the response of a system to perturbations is a key challenge in mathematical and natural sciences. Under suitable conditions on the nature of the system, of the perturbation, and of the observables of interest, response theories…

Statistical Mechanics · Physics 2017-09-13 Valerio Lucarini , Jeroen Wouters

In the chaotic quantization approach one replaces the Gaussian white noise of the Parisi-Wu approach of stochastic quantization by a deterministic chaotic process on a very small scale. We consider suitable coupled chaotic noise processes…

High Energy Physics - Theory · Physics 2007-05-23 Christian Beck

Representing and quantifying uncertainty in physical parameterisations is a central challenge in weather and climate modelling, and approaches are often developed separately for different timescales. Here, we introduce a unified framework…

Atmospheric and Oceanic Physics · Physics 2025-12-01 Laura A. Mansfield , Hannah M. Christensen

We apply the stochastic thermodynamics formalism to describe the dynamics of systems of complex Langevin and Fokker-Planck equations. We provide in particular a simple and general recipe to calculate thermodynamical currents, dissipated and…

Statistical Mechanics · Physics 2019-01-30 Simone Borlenghi , Anna Delin

Stochastic forces in natural systems are rarely isotropic. From hydrodynamically coupled colloids to chemical reaction networks, noise contributions are inherently correlated. Together with internal interactions and changing environments,…

Statistical Mechanics · Physics 2026-05-14 Andrea Marchetti , Daniel Maria Busiello , Giorgio Nicoletti

Data-driven control based on the fundamental lemma by Willems et al. is frequently considered for deterministic LTI systems subject to measurement noise. However, besides measurement noise, stochastic disturbances might also directly affect…

Systems and Control · Electrical Eng. & Systems 2023-08-14 Guanru Pan , Ruchuan Ou , Timm Faulwasser

Many time series are effectively generated by a combination of deterministic continuous flows along with discrete jumps sparked by stochastic events. However, we usually do not have the equation of motion describing the flows, or how they…

Machine Learning · Computer Science 2020-01-09 Junteng Jia , Austin R. Benson

Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influence of high-dimensional fast processes. In practice, observations are often limited to a…

Machine Learning · Statistics 2026-05-12 Anan Saha , Arnab Ganguly

In stochastic thermodynamics standard concepts from macroscopic thermodynamics, such as heat, work, and entropy production, are generalized to small fluctuating systems by defining them on a trajectory-wise level. In Langevin systems with…

Statistical Mechanics · Physics 2019-09-04 Stefano Bo , Soon Hoe Lim , Ralf Eichhorn

In {\em{Holm}, Proc. Roy. Soc. A 471 (2015)} stochastic fluid equations were derived by employing a variational principle with an assumed stochastic Lagrangian particle dynamics. Here we show that the same stochastic Lagrangian dynamics…

Analysis of PDEs · Mathematics 2017-10-25 Colin J Cotter , Georg A Gottwald , Darryl D Holm

This paper considers stochastic population dynamics driven by Levy noise. The contributions of this paper lie in that (a) Using Khasminskii-Mao theorem, we show that the stochastic differential equation associated with the model has a…

Probability · Mathematics 2011-05-09 Jianhai Bao , Chenggui Yuan

Proceeding from the concept of rational expectations, a new dynamic model of supply and demand in a single market with one supplier, one buyer, and one kind of commodity is developed. Unlike the cob-web dynamic theories with adaptive…

General Physics · Physics 2007-05-23 V. Granik , A. Granik

Modeling real-world systems requires accounting for noise - whether it arises from unpredictable fluctuations in financial markets, irregular rhythms in biological systems, or environmental variability in ecosystems. While the behavior of…

Machine Learning · Computer Science 2026-04-08 Matteo Bosso , Giovanni Franzese , Kushal Swamy , Maarten Theulings , Alejandro M. Aragón , Farbod Alijani

We explore a class of hybrid (piecewise deterministic) systems characterized by a large number of individuals inhabiting an environment whose state is described by a set of continuous variables. We use analytical and numerical methods from…

Statistical Mechanics · Physics 2012-09-10 John Realpe-Gomez , Tobias Galla , Alan J. McKane

Physical parameterizations are used as representations of unresolved subgrid processes within weather and global climate models or coarse-scale turbulent models, whose resolutions are too coarse to resolve small-scale processes. These…

Machine Learning · Statistics 2022-10-27 Mohamed Aziz Bhouri , Pierre Gentine

We present approaches for the study of fluid-structure interactions subject to thermal fluctuations. A mixed mechanical description is utilized combining Eulerian and Lagrangian reference frames. We establish general conditions for…

Soft Condensed Matter · Physics 2023-02-28 Paul J. Atzberger

Turbulent dynamical systems characterized by both a high-dimensional phase space and a large number of instabilities are ubiquitous among many complex systems in science and engineering. The existence of a strange attractor in the turbulent…

Fluid Dynamics · Physics 2018-02-23 Andrew J. Majda , Di Qi

The understanding of the fundamental properties of the climate system has long benefitted from the use of simple numerical models able to parsimoniously represent the essential ingredients of its processes. Here we introduce a new model for…

Chaotic Dynamics · Physics 2020-11-16 Gabriele Vissio , Valerio Lucarini

Stochastic versions of a classical model for natural ventilation are proposed and investigated to demonstrate the effect of random fluctuations on stability and predictability. In a stochastic context, the well-known deterministic result…

Fluid Dynamics · Physics 2023-08-17 Veronica Andrian , John Craske

We study stochastic model reduction for evolution equations in infinite dimensional Hilbert spaces, and show the convergence to the reduced equations via abstract results of Wong-Zakai type for stochastic equations driven by a scaled…

Probability · Mathematics 2022-05-13 Sigurd Assing , Franco Flandoli , Umberto Pappalettera
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