Results 261 to 270 of about 44,590 (312)

Dynamical analysis and exact solitary wave solutions of [Formula: see text]-dimensional integro-differential Jaulent-Miodek equation using two analytical schemes. [PDF]

Sci Rep
Nasir B, Abbas M, Yousaf MZ, Nazir T, El-Rahman MA, Birhanu A.   +5 more
europepmc   +1 more source

Identifying stochastic dynamics from non-sequential data (DyNoSeD). [PDF]

Chaos
Lu Z, Kuśmierz Ł, Mihalas S.
europepmc   +1 more source

Statistical learning of stochastic complex systems via the Yau-Yau nonlinear filter. [PDF]

Innovation (Camb)
Xu S, Wang Y, Wu S, Wang Y, Dong A, Shing-Toung Yau S, Yau ST, Wu R.   +7 more
europepmc   +1 more source

New analytical wave solutions for the gardner equation via the Riccati-modified extended simple equation method. [PDF]

Sci Rep
Jawarneh Y, Almusawa MY, Haleemzai I, Hakami AH, Ahmadini AAH, Albaity A.   +5 more
europepmc   +1 more source

Stochastic modeling of epigenetic memory. [PDF]

NPJ Syst Biol Appl
Bruno S, Del Vecchio D.
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Contraction of stochastic differential equations

Communications in Nonlinear Science and Numerical Simulation, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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On solving stochastic differential equations

Monte Carlo Methods and Applications, 2019
Abstract This paper proposes a new approach to solving Ito stochastic differential equations. It is based on the well-known Monte Carlo methods for solving integral equations (Neumann–Ulam scheme, Markov chain Monte Carlo). The estimates of the solution for a wide class of equations do not have a bias, which distinguishes them from ...
Sergej M. Ermakov, Anna A. Pogosian
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Stochastic differential equations

Physics Reports, 1976
Abstract In chapter I stochastic differential equations are defined and classified, and their occurrence in physics is reviewed. In chapter II it is shown for linear equation show a differential equation for the averaged solution is obtained by expanding in ατ c , where α measures the size of the fluctuations and τ c their autocorrelation time. This
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Stochastic differential equations

2011
In this chapter we present some basic results on stochastic differential equations, hereafter shortened to SDEs, and we examine the connection to the theory of parabolic partial differential equations.
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Numerical Methods for Stochastic Differential Equations

Stochastic Hydrology and Hydraulics, 1991
Numerical methods for stochastic differential equations, including Taylor expansion approximations, Runge-Kutta like methods and implicit methods, are summarized. Important differences between simulation techniques with respect to the strong (pathwise) and the weak (distributional) approximation criteria are discussed. Applications to the visualization
Kloeden, Peter E., Platen, Eckhard
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