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

Random Walks and Markov Chains Poisson and Exponential Processes Brownian Motion and Diffusion Monte Carlo Sampling Variance Reduction Techniques Bayesian Inference for Model Parameters Bayesian Model Checking Polynomial Chaos and UQ Sensitivity Analysis and Sobol Indices Regression and Meta-modeling Credible Intervals and Hypothesis Testing Model Calibration under Uncertainty Scenario Analysis and Stress Testing Stochastic Differential Equations Temporal and Spatial Uncertainty Modeling

8Data Structures, Algorithms, and Simulation Efficiency

9Simulation Programming Languages and Frameworks

10Verification, Validation, and Experiment Design

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/Stochastic Processes and Uncertainty Quantification

Stochastic Processes and Uncertainty Quantification

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Incorporate randomness and quantify uncertainty to make robust predictions from simulations.

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Monte Carlo Sampling

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