About this course
This course provides a comprehensive, progressive exploration of modeling and simulation techniques within computer science. Students will start with foundational concepts of model building, abstraction, and validation, then advance through mathematical underpinnings, discrete-event and agent-based paradigms, continuous system dynamics, numerical methods, and stochastic approaches. The curriculum...
What you'll learn
- Construct and formalize models using discrete-event, agent-based, and continuous paradigms
- Apply mathematical and numerical methods to build stable, accurate simulations
- Implement simulations in modern programming environments and simulation frameworks
- Validate and verify models against data and design rigorous experiments
- Quantify uncertainty and use stochastic methods for sensitivity and risk analysis
- Optimize simulation performance using efficient algorithms, data structures, and profiling
- Scale simulations on parallel and high-performance computing platforms
- Manage simulation data, ensure reproducibility, and track experiments
- Visualize results and communicate model insights to technical and non-technical audiences
Prerequisites
Programming experience (Python/C++/Java), undergraduate-level calculus and linear algebra, and basic probability/statistics.
- Level
- Intermediate· Level
- Duration
- 12 weeks· Duration
- Language
- English· Language
- Modules
- 15· Modules
Skills you'll gain
- Model formulation
- Numerical methods
- Stochastic modeling
- Simulation programming
- Performance profiling and optimization
- Parallel and distributed computing
- Experimental design
- Data management and reproducibility
- Visualization and communication
- Uncertainty quantification
What you'll study
15 modules — work at your own pace.
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