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

Floating Point Arithmetic Linear Systems Solvers Numerical Integration Methods ODE and PDE Solvers Finite Difference Method Finite Element Method Stability and Convergence Criteria Mesh Generation and Adaptivity Time-Stepping Schemes Spectral Methods for PDEs Interpolation and Approximation Error Estimation and Adaptive Refinement Parallel Numerical Computation Software Libraries for Numerical Computing Benchmarking and Verification of Numerical Models

7Stochastic Processes and Uncertainty Quantification

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/Numerical Methods and Scientific Computing for Simulation

Numerical Methods and Scientific Computing for Simulation

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Learn the numerical techniques that underlie stable and accurate simulations across domains.

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Stability and Convergence Criteria

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