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Small-scale effects of turbulent mixing are numerically investigated by applying the map-based, stochastic, one-dimensional turbulence (ODT) model to confined planar jets. The model validation is carried out for the momentum transport by…

Fluid Dynamics · Physics 2019-04-19 Marten Klein , Christian Zenker , Heiko Schmidt

We consider the dynamical system consisting of a quantum degree of freedom $A$ interacting with $N$ quantum oscillators described by the Lagrangian \bq L = {1\over 2}\dot{A}^2 + \sum_{i=1}^{N} \left\{{1\over 2}\dot{x}_i^2 - {1\over 2}( m^2…

In modern deep learning, there is a recent and growing literature on the interplay between large-width asymptotic properties of deep Gaussian neural networks (NNs), i.e. deep NNs with Gaussian-distributed weights, and Gaussian stochastic…

Machine Learning · Computer Science 2022-06-27 Stefano Favaro , Sandra Fortini , Stefano Peluchetti

We consider a particle, confined to a moving harmonic potential, under the influence of friction and external asymmetric Poissonian shot noise (PSN). We study the fluctuations of the work done to maintain this system in a nonequilibrium…

Statistical Mechanics · Physics 2009-04-01 A. Baule , E. G. D. Cohen

Motivated by estimating the lead-lag relationships in high-frequency financial data, we propose noisy bivariate Neyman-Scott point processes with gamma kernels (NBNSP-G). NBNSP-G tolerates noises that are not necessarily Poissonian and has…

Statistics Theory · Mathematics 2026-01-06 Takaaki Shiotani , Nakahiro Yoshida

In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neural networks, as the number or neurons (ie, the size of the…

Machine Learning · Statistics 2020-07-15 Valentin De Bortoli , Alain Durmus , Xavier Fontaine , Umut Simsekli

We consider a linear elliptic system in divergence form with random coefficients and study the random fluctuations of large-scale averages of the field and the flux of the solution operator. In the context of the random conductance model,…

Analysis of PDEs · Mathematics 2019-10-25 Mitia Duerinckx , Antoine Gloria , Felix Otto

An ensemble of pulse-coupled phase-oscillators is thoroughly analysed in the presence of a mean-field coupling and a dispersion of their natural frequencies. In spite of the analogies with the Kuramoto setup, a much richer scenario is…

Chaotic Dynamics · Physics 2017-11-06 Ekkehard Ullner , Antonio Politi

Assuming frictionless trading, classical stochastic portfolio theory (SPT) provides relative arbitrage strategies. However, the costs associated with real-world execution are state-dependent, volatile, and under increasing stress during…

Portfolio Management · Quantitative Finance 2025-07-15 Nader Karimi , Erfan Salavati

This paper presents some asymptotic results for statistics of Brownian semi-stationary (BSS) processes. More precisely, we consider power variations of BSS processes, which are based on high frequency (possibly higher order) differences of…

Probability · Mathematics 2013-06-04 José Manuel Corcuera , Emil Hedevang , Mikko S. Pakkanen , Mark Podolskij

-The fluctuation effect of gradient expectation and variance caused by parameter update between consecutive iterations is neglected or confusing by current mainstream gradient optimization algorithms. The work in this paper remedy this…

Machine Learning · Statistics 2021-10-08 Aixiang Chen

The dynamics of a one-dimensional stochastic model is studied in presence of an absorbing boundary. The distribution of fluctuations is analytically characterized within the generalized van Kampen expansion, accounting for higher order…

Statistical Mechanics · Physics 2015-05-28 Claudia Cianci , Francesca Di Patti , Duccio Fanelli

We consider an O(N) model coupled self-consistently to gravity in the semiclassical approximation, where the field is subject to `new inflation' type initial conditions. We study the dynamics self-consistently and non-perturbatively with…

High Energy Physics - Phenomenology · Physics 2011-04-15 D. Boyanovsky , D. Cormier , H. J. de Vega , R. Holman , S. P. Kumar

An optimized perturbation theory (OPT) at finite temperature T, which resums higher order terms in the naive perturbation, is developed in O(N) phi^4 theory. It is proved that (i) the renormalization of the ultra-violet divergences can be…

High Energy Physics - Phenomenology · Physics 2011-08-12 S. Chiku , T. Hatsuda

We study the O(2N) model at criticality in three dimensions in the double scaling limit of large N and large charge. We show that the large-charge expansion is an asymptotic series, and we use resurgence techniques to study the…

High Energy Physics - Theory · Physics 2021-05-19 Nicola Dondi , Ioannis Kalogerakis , Domenico Orlando , Susanne Reffert

We study the point process $W$ in $\mathbb{R}^d$ obtained by adding an independent Gaussian vector to each point in $\mathbb{Z}^d$. Our main concern is the asymptotic size of fluctuations of the linear statistics in the large volume limit,…

Probability · Mathematics 2022-01-26 Oren Yakir

We develop a multi-reference perturbation theory for electronic structure calculations based on symmetries of the Hamiltonian. The reference Hamiltonian in the symmetry-based perturbation theory (SBPT) is chosen such that it possesses more…

Quantum Physics · Physics 2026-03-10 Hiromichi Nishimura , Nam Nguyen , Tanvi Gujarati , Mario Motta

We construct Langevin equations describing the fluctuations of the tensor order parameter $Q_{\alpha\beta}$ in nematic liquid crystals by adding noise terms to time-dependent variational equations that follow from the Ginzburg-Landau-de…

Soft Condensed Matter · Physics 2010-01-07 A. K. Bhattacharjee , Gautam I. Menon , R. Adhikari

The local number variance associated with a spherical sampling window of radius $R$ enables a classification of many-particle systems in $d$-dimensional Euclidean space according to the degree to which large-scale density fluctuations are…

Statistical Mechanics · Physics 2021-05-12 Salvatore Torquato , Jaeuk Kim , Michael A. Klatt

Imperfections and uncertainties in forecast models are often represented in ensemble prediction systems by stochastic perturbations of model equations. In this article, we present a new technique to generate model perturbations. The…

Atmospheric and Oceanic Physics · Physics 2023-11-28 Michael Tsyrulnikov , Elena Astakhova , Dmitry Gayfulin