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The asymptotic log-Harnack inequality is established for several different models of stochastic differential systems with infinite memory: non-degenerate SDEs, Neutral SDEs, semi-linear SPDEs, and stochastic Hamiltonian systems. As…

Probability · Mathematics 2018-09-10 Jianhai Bao , Feng-Yu Wang , Chenggui Yuan

We present a detailed study of a scalar differential equation with threshold state-dependent delayed feedback. This equation arises as a simplification of a gene regulatory model. There are two monotone nonlinearities in the model: one…

Dynamical Systems · Mathematics 2025-04-29 Tomas Gedeon , Antony R. Humphries , Michael C. Mackey , Hans-Otto Walther , Zhao Wang

We consider the stochastically driven one dimensional nonlinear oscillator $\ddot{x}+2\Gamma\dot{x}+\omega^2_0 x+\lambda x^3 = f(t)$ where f(t) is a Gaussian noise which, for the bulk of the work, is delta correlated (white noise). We…

Statistical Mechanics · Physics 2022-08-04 Akshay Pal , Jayanta Kumar Bhattacharjee

The uniform static spin susceptibility in the paraphase of the one-band Hubbard model is calculated within a theory of magnetic short--range order (SRO) which extends the four-field slave-boson functional-integral approach by the trans-…

Condensed Matter · Physics 2009-10-28 U. Trapper , D. Ihle , H. Fehske

New types of stationary solutions of a one-dimensional driven sixth-order Cahn-Hilliard type equation that arises as a model for epitaxially growing nano-structures such as quantum dots, are derived by an extension of the method of matched…

Mathematical Physics · Physics 2008-11-14 M. D. Korzec , P. L. Evans , A. Münch , B. Wagner

We examine the long-wavelength current response in anisotropic superconductors and show how the field-dependence of the Meissner penetration length can be used to detect the structure of the order parameter. Nodes in the excitation gap lead…

Condensed Matter · Physics 2016-08-31 D. Xu , S. K. Yip , J. A. Sauls

Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent states and mean parameter of the probability distribution of…

Machine Learning · Statistics 2019-12-23 Aliaksandr Hubin

Parameter-dependent models arise in many contexts such as uncertainty quantification, sensitivity analysis, inverse problems or optimization. Parametric or uncertainty analyses usually require the evaluation of an output of a model for many…

Numerical Analysis · Mathematics 2018-10-22 Anthony Nouy

We derive the first four terms in a series for the order paramater (the stationary activity density rho) in the supercritical regime of a one-dimensional stochastic sandpile; in the two-dimensional case the first three terms are reported.…

Statistical Mechanics · Physics 2009-11-10 Ronaldo Vidigal , Ronald Dickman

Chemical affinities are responsible for driving active matter systems out of equilibrium. At the nano-scale, molecular machines interact with the surrounding environment and are subjected to external forces. The mechano-chemical coupling…

Soft Condensed Matter · Physics 2025-01-03 Michalis Chatzittofi , Jaime Agudo-Canalejo , Ramin Golestanian

Nonlinear electrical response permits a unique window into effects of band structure geometry. It can be calculated either starting from a Boltzmann approach for small frequencies, or using Kubo's formula for resonances at finite frequency.…

Mesoscale and Nanoscale Physics · Physics 2023-04-26 Daniel Kaplan , Tobias Holder , Binghai Yan

This work presents a fully theoretical and self consistent framework for calculating the third-order nonlinear susceptibility of CdSe/ZnS--MOF composite quantum dots. The approach unifies finite-potential quantum confinement,the Liouville…

Mesoscale and Nanoscale Physics · Physics 2025-11-06 Jingxu Wu , Yifan Yang , Jie Shi , Yuwei Yin , Yifan He , Chenjia Li

This paper is concerned with a characterization of the observability for a continuous-time hidden Markov model where the state evolves as a general continuous-time Markov process and the observation process is modeled as nonlinear function…

Probability · Mathematics 2020-02-25 Jin W. Kim , Prashant G. Mehta

The paper deals with the problem of output regulation in a "non-equilibrium" context for a special class of multivariable nonlinear systems stabilizable by high-gain feedback. A post-processing internal model design suitable for the…

Systems and Control · Electrical Eng. & Systems 2020-04-22 Michelangelo Bin , Lorenzo Marconi

We study the statistical inference of nonlinear stochastic approximation algorithms utilizing a single trajectory of Markovian data. Our methodology has practical applications in various scenarios, such as Stochastic Gradient Descent (SGD)…

Statistics Theory · Mathematics 2023-02-21 Xiang Li , Jiadong Liang , Zhihua Zhang

The methods of the probability theory have been used in order to build up a new model of hysteresis. It turns out that the reversal points of the control parameter (e. g., the magnetic field) are Markov points which determine the stochastic…

Statistical Mechanics · Physics 2009-10-31 L. Pal

This paper considers a class of nonlinear time harmonic Maxwell systems at fixed frequency, with nonlinear terms taking the form $\mathscr{X}(x,|\vec E(x)|^2)\vec E(x)$, $\mathscr{Y}(x,|\vec H(x)|^2)\vec H(x)$, such that $\mathscr{X}(x,s)$,…

Analysis of PDEs · Mathematics 2018-04-26 Cătălin I. Cârstea

The paper introduces a new regression model designed for situations where both the response and covariates are non-stationary extremes. This method is specifically designed for situations where both the response variable and covariates are…

Methodology · Statistics 2025-06-26 Amina El Bernoussi , Mohamed El Arrouchi

We present a new methodology for studying non-Hamiltonian nonlinear systems based on an information theoretic extension of a renormalization group technique using a modified maximum entropy principle. We obtain a rigorous dimensionally…

Computational Physics · Physics 2013-06-28 M. Schmuck , M. Pradas , S. Kalliadasis , G. A. Pavliotis

We introduce a new method of estimation of parameters in semiparametric and nonparametric models. The method is based on estimating equations that are $U$-statistics in the observations. The $U$-statistics are based on higher order…

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