Numerical simulation of nonlinear dynamical systems driven by commutative noise
Journal Article
·
· Journal of Computational Physics
- Institute for Cybernetics, Mathematics and Physics, Department of Interdisciplinary Mathematics, Havana (Cuba)
- Granma University, Department of Mathematics and Computation, Bayamo MN (Cuba)
The local linearization (LL) approach has become an effective technique for the numerical integration of ordinary, random and stochastic differential equations. One of the reasons for this success is that the LL method achieves a convenient trade-off between numerical stability and computational cost. Besides, the LL method reproduces well the dynamics of nonlinear equations for which other classical methods fail. However, in the stochastic case, most of the reported works has been focused in Stochastic Differential Equations (SDE) driven by additive noise. This limits the applicability of the LL method since there is a number of interesting dynamics observed in equations with multiplicative noise. On the other hand, recent results show that commutative noise SDEs can be transformed into a random differential equation (RDE) by means of a random diffeomorfism (conjugacy). This paper takes advantages of such conjugacy property and the LL approach for defining a LL scheme for SDEs driven by commutative noise. The performance of the proposed method is illustrated by means of numerical simulations.
- OSTI ID:
- 21028271
- Journal Information:
- Journal of Computational Physics, Journal Name: Journal of Computational Physics Journal Issue: 2 Vol. 226; ISSN JCTPAH; ISSN 0021-9991
- Country of Publication:
- United States
- Language:
- English
Similar Records
Probabilistic density function method for nonlinear dynamical systems driven by colored noise
Response of MDOF strongly nonlinear systems to fractional Gaussian noises
Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning
Journal Article
·
Sun May 01 00:00:00 EDT 2016
· Physical Review E
·
OSTI ID:1327085
Response of MDOF strongly nonlinear systems to fractional Gaussian noises
Journal Article
·
Mon Aug 15 00:00:00 EDT 2016
· Chaos (Woodbury, N. Y.)
·
OSTI ID:22596479
Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning
Journal Article
·
Tue Feb 14 19:00:00 EST 2023
· Chaos: An Interdisciplinary Journal of Nonlinear Science
·
OSTI ID:2564735