Second Harmonics Effects in Random Duffing Oscillators

We consider a stochastic model for Duffing oscillators, where the nonlinearity and the randomness are scaled in such a way that they interact strongly. A typical feature is the appearance of second harmonics effects. An asymptotic statistical analysis for these oscillators is performed in the diffusion limit, when a suitable absorbing boundary condition is imposed, according to the underlying physical problem. The related Fokker-Planck equation has been numerically solved to obtain the first two moments of the oscillator's displacement from its rest-position. Dependence on the nonlinearity strength and on the location of the absorbing boundary has also been investigated. Such results have been compared with those computed solving the corresponding stochastic Ito differential equations by a Monte Carlo method, where quasi-random sequences of numbers have been efficiently used.

[1]  R. L. Stratonovich,et al.  Topics in the theory of random noise , 1967 .

[2]  Malcolm R Leadbetter,et al.  Stationary and Related Stochastic Processes: Sample Function Properties and Their Applications , 1967 .

[3]  高橋 利衛,et al.  〔47〕非線型振動論 : 〔Nonlinear Vibrations in Mechanical and Electrical Systems, J.J.Stoker, Interscience Pub.N.Y., 1950,本文264ページ, 5$〕(1.應用力學および機械力學) , 1951 .

[4]  R. Spigler,et al.  Fast simulations of stochastic dynamical systems , 2005 .

[5]  Russel E. Caflisch,et al.  Quasi-Random Sequences and Their Discrepancies , 1994, SIAM J. Sci. Comput..

[6]  W. Feller TWO SINGULAR DIFFUSION PROBLEMS , 1951 .

[7]  J. J. Stoker Nonlinear Vibrations in Mechanical and Electrical Systems , 1950 .

[8]  Joseph B. Keller,et al.  Stochastic Differential Equations with Applications to Random Harmonic Oscillators and Wave Propagation in Random Media , 1971 .

[9]  R. Caflisch Monte Carlo and quasi-Monte Carlo methods , 1998, Acta Numerica.

[10]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations , 2001, SIAM Rev..

[11]  Faysal El Khettabi,et al.  Quasi-Monte Carlo Simulation of Diffusion , 1999, J. Complex..

[12]  A. Yaglom,et al.  An Introduction to the Theory of Stationary Random Functions , 1963 .

[13]  George Papanicolaou,et al.  Wave Propagation in a One-Dimensional Random Medium , 1971 .

[14]  Maurice Fréchet,et al.  Les probabilités continues „en chaîne” , 1933 .

[15]  R. G. Medhurst,et al.  Topics in the Theory of Random Noise , 1969 .

[16]  Peter Mathé,et al.  On quasi-Monte Carlo simulation of stochastic differential equations , 1997, Math. Comput..

[17]  Honeycutt Stochastic Runge-Kutta algorithms. II. Colored noise. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[18]  Shigeyoshi Ogawa,et al.  A quasi-random walk method for one-dimensional reaction-diffusion equations , 2003, Math. Comput. Simul..

[19]  Russel E. Caflisch,et al.  A quasi-Monte Carlo approach to particle simulation of the heat equation , 1993 .

[20]  Harald Niederreiter,et al.  Random number generation and Quasi-Monte Carlo methods , 1992, CBMS-NSF regional conference series in applied mathematics.

[21]  E. Platen An introduction to numerical methods for stochastic differential equations , 1999, Acta Numerica.

[22]  A. Owen,et al.  Valuation of mortgage-backed securities using Brownian bridges to reduce effective dimension , 1997 .

[23]  Renato Spigler A Stochastic Model for Nonlinear Oscillations of Duffing Type , 1985 .

[24]  W. Feller THE PARABOLIC DIFFERENTIAL EQUATIONS AND THE ASSOCIATED SEMI-GROUPS OF TRANSFORMATIONS , 1952 .

[25]  harald Cramer,et al.  Stationary And Related Stochastic Processes , 1967 .