Explore MATLAB source code curated for "随机微分方程" with clean implementations, documentation, and examples.
Solving stochastic differential equations presents significant computational challenges. This foreign-developed toolbox provides comprehensive implementations of various numerical methods for SDE solutions, including key algorithms like Euler-Maruyama and Milstein methods. We invite fellow researchers to utilize this resource and engage in collaborative discussions to address implementation challenges.
MATLAB Dynamical Systems and Time Series Analysis Toolbox: This comprehensive toolbox enables analysis of dynamical systems and time series data, supporting customization for Ordinary Differential Equations (ODE) and Stochastic Differential Equations (SDE). All analytical methods are encapsulated within modular functions accessible via command-line interface or graphical user interface (GUI). Key functionalities include: ODE/SDE solvers, map integration for time series analysis, filtering techniques, normalization/standardization procedures, histogram/2D histogram visualization, Auto-Correlation Function (ACF), Mutual Information Index (MAI), Fast Fourier Transform (FFT), maximum Lyapunov exponent calculation, and pattern recognition algorithms. Dynamical system analysis features comprise Poincaré section generation, bifurcation diagram plotting, and Lyapunov exponent computation routines.
A Representative Case Study for Solving Systems of Stochastic Differential Equations with Code Implementation Insights