Modeling and Simulation in computer science

Chapters

1Foundations of Modeling in Computer Science

2Mathematical Foundations for Simulation

3Discrete-Event Simulation Fundamentals

4Agent-Based Modeling and Complex Systems

5System Dynamics and Continuous Modeling

6Numerical Methods and Scientific Computing for Simulation

7Stochastic Processes and Uncertainty Quantification

8Data Structures, Algorithms, and Simulation Efficiency

9Simulation Programming Languages and Frameworks

10Verification, Validation, and Experiment Design

Verification Techniques and Checks Validation Against Real Data Experimental Design in Simulation Sensitivity and Uncertainty Analyses Confidence Assessment Hypothesis Testing in Simulation Reproducibility and Replicability Debugging and Fault Isolation Model Calibration Procedures Calibration vs Validation Data Sets Cross-Validation Approaches Statistical Metamodels Surrogate Modeling Protocols for Repeated Runs Documentation and Audit Trails

11Performance Analysis and Optimization of Simulations

12High-Performance Computing and Parallel Simulation

13Data Management, Reproducibility, and Experiment Tracking

14Visualization and Communication of Simulation Results

15Applications and Case Studies in Modeling and Simulation

Courses/Modeling and Simulation in computer science/Verification, Validation, and Experiment Design

Verification, Validation, and Experiment Design

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Develop rigorous processes to ensure models are correct, credible, and useful for decision-making.

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Hypothesis Testing in Simulation

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