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We develop a provably efficient importance sampling scheme that estimates exit probabilities of solutions to small-noise stochastic reaction-diffusion equations from scaled neighborhoods of a stable equilibrium. The moderate deviation…

Probability · Mathematics 2023-10-24 Ioannis Gasteratos , Michael Salins , Konstantinos Spiliopoulos

Stochastic approximation (SA) is a classical approach for stochastic convex optimization. Previous studies have demonstrated that the convergence rate of SA can be improved by introducing either smoothness or strong convexity condition. In…

Machine Learning · Computer Science 2019-01-29 Lijun Zhang , Zhi-Hua Zhou

Dynamics of a dissipative two-level system is studied using quantum relaxation theory. This calculation for the first time goes beyond the commonly used dilute bounce gas approximation (DBGA), even for strong damping. The new results…

Condensed Matter · Physics 2011-12-13 Tabish Qureshi

We present a derivation of the Redfield formalism for treating the dissipative dynamics of a time-dependent quantum system coupled to a classical environment. We compare such a formalism with the master equation approach where the…

In this paper, we study the diffusion approximation for slow-fast stochastic differential equations with state-dependent switching, where the slow component $X^{\varepsilon}$ is the solution of a stochastic differential equation with…

Probability · Mathematics 2025-03-12 Xiaobin Sun , Jue Wang , Yingchao Xie

This paper deals with the approximation and homogenization of thermoelastic wave model. First, we study the homogenization problem of a weakly coupled thermoelastic wave model with rapidly varying coefficients, using a semigroup approach,…

Analysis of PDEs · Mathematics 2023-06-29 Salem Nafiri

The applicability conditions of a recently reported Central Limit Theorem-based approximation method in statistical physics are investigated and rigorously determined. The failure of this method at low and intermediate temperature is proved…

Statistical Mechanics · Physics 2012-03-21 Bruno Leggio , Oleg Lychkovskiy , Antonino Messina

This work is devoted to examining qualitative properties of dynamic systems, in particular, limit cycles of stochastic differential equations with both rapid switching and small diffusion. The systems are featured by multi-scale…

Dynamical Systems · Mathematics 2017-07-20 Dang H. Nguyen , Nguyen H. Du , George Yin

We present an ab initio stochastic method for calculating thermal properties of a trapped, 1D Bose-gas covering the whole range from weak to strong interactions. Discretization of the problem results in a Bose-Hubbard-like Hamiltonian,…

Statistical Mechanics · Physics 2009-11-10 B. Schmidt , L. I. Plimak , M. Fleischhauer

In this tutorial, three examples of stochastic systems are considered: A strongly-damped oscillator, a weakly-damped oscillator and an undamped oscillator (integrator) driven by noise. The evolution of these systems is characterized by the…

Statistical Mechanics · Physics 2022-02-02 C. J. McKinstrie , T. J. Stirling , A. S. Helmy

The departure of a granular gas in the instable region of parameters from the initial homogeneous cooling state is studied. Results from Molecular Dynamics and from Direct Monte Carlo simulation of the Boltzmann equation are compared. It is…

Statistical Mechanics · Physics 2009-11-10 J. Javier Brey , M. J. Ruiz-Montero

In this paper, we study the dynamics of a linear control system with given state feedback control law in the presence of fast periodic sampling at temporal frequency $1/\delta$ ($0 < \delta \ll 1$), together with small white noise…

Probability · Mathematics 2021-10-15 Shivam Dhama , Chetan D. Pahlajani

We are interested in numerically solving a transitional model derived from the Bloch model. The Bloch equation describes the time evolution of the density matrix of a quantum system forced by an electromagnetic wave. In a high frequency and…

Analysis of PDEs · Mathematics 2023-08-02 Brigitte Bidégaray-Fesquet , Clément Jourdana , Léopold Trémant

Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we seek to balance the fact that attenuating step-size is…

Signal Processing · Electrical Eng. & Systems 2020-07-10 Zhan Gao , Alec Koppel , Alejandro Ribeiro

A recently developed approach to the thermodynamics of open quantum systems, on the basis of the principle of minimal dissipation, is applied to the spin-boson model. Employing a numerically exact quantum dynamical treatment based on the…

Quantum Physics · Physics 2024-08-30 Salvatore Gatto , Alessandra Colla , Heinz-Peter Breuer , Michael Thoss

We develop here the method for obtaining approximate stability boundaries in the space of parameters for systems with parametric excitation. The monodromy (Floquet) matrix of linearized system is found by averaging method. For system with 2…

Dynamical Systems · Mathematics 2019-12-24 Anton O. Belyakov , Alexander P. Seyranian

The stochastic dynamics of the damped harmonic oscillator in a heat bath is simulated with an algorithm that is exact for time steps of arbitrary size. Exact analytical results are given for correlation functions and power spectra in the…

Data Analysis, Statistics and Probability · Physics 2011-04-06 Simon F. Norrelykke , Henrik Flyvbjerg

Within the lowest-order Born approximation, we present an exact calculation of the time dynamics of the spin-boson model in the Ohmic regime. We observe non-Markovian effects at zero temperature that scale with the system-bath coupling…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 Daniel Loss , David P. DiVincenzo

We analyze a system of nonlinear stochastic partial differential equations (SPDEs) of mixed elliptic-parabolic type that models the propagation of electric signals and their effect on the deformation of cardiac tissue. The system governs…

Analysis of PDEs · Mathematics 2024-05-29 Mostafa Bendahmane , Kenneth H. Karlsen , Fatima Mroue

We present new algorithms and fast implementations to find efficient approximations for modelling stochastic processes. For many numerical computations it is essential to develop finite approximations for stochastic processes. While the…

Optimization and Control · Mathematics 2020-12-03 Kipngeno Benard Kirui , Georg Ch. Pflug , Alois Pichler
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