ABSTRACT
Numerical methods for stochastic differential equations are summarized, including Taylor expansion approximations, Runge-Kutta-like methods, and implicit methods. Important differences between simulation techniques with respect to the strong (pathwise) and the weak (distributional) approximation criteria are discussed. Applications to the visualization of nonlinear stochastic dynamics, the computation of Lyapunov exponents, and stochastic bifurcations are also presented.
