Scientific Computing & Simulation

By the end, you can

• Identify when simulation is the right tool versus a closed-form solution.
• Explain how Monte Carlo estimation works (random darts, 4·inside/total for π).
• Apply the σ/√N error law to compute how many more samples are needed to reach a target accuracy.
• Describe why Monte Carlo's convergence rate is advantageous in high dimensions.
• Name three variance-reduction techniques and explain what factor each one reduces.
• Trace one iteration of the discrete-event next-event loop (pop, jump, handle, schedule, record).
• State the stability condition for an M/M/1 queue and apply Little's Law L = λW.
• List the three Boids rules and explain how emergence arises in agent-based models.
• Explain why fixing a random seed ensures reproducibility and why confidence intervals are required.