Simulink Best Practices for Large-Scale Modeling
These best practices cover modeling patterns, tools, and processes that have proven to yield Simulink® models that are efficient, scalable, and maintainable. The specific topics covered are:
- Componentization and interfaces
- Collaboration with MATLAB® Projects
- Data management
- Simulation performance improvement
Best Practices for Large-Scale Modeling: Architecture and Performance
Explore techniques for scalable Simulink models, covering componentization, data, interfaces, collaboration, performance, and maintainable workflows.
Simulink Componentization: Model References and Interfaces
Build scalable Simulink architectures with model references, libraries, and subsystems to improve reuse, collaboration, testing, and performance.
Collaborative Simulink Development with MATLAB Projects and Git
Use MATLAB Projects to collaborate on Simulink models with source control, dependency analysis, testing, CI/CD, and maintainable workflows.
Simulink Data Dictionaries: Best Practices for Design Data Management
Manage Simulink design data with data dictionaries, model workspaces, arguments, and value types for scalable, consistent Model-Based Design.
Simulation Speedup in Simulink
Larger models tend to mean longer simulation times. Explore how to optimize models and settings to speed up simulations.
