: Algorithms generate numbers that mimic true randomness. Linear Congruential Generator (LCG) : Formulated as:
: Represent a system at a specific point in time. Time is not a variable. Example: Monte Carlo structural stress analysis.
Historically popular, LCGs calculate sequential random numbers using modular arithmetic:
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When models are too vast for a single machine, distributed simulation splits the workload across multiple computers. : Algorithms generate numbers that mimic true randomness
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Euler’s Method : Low computational overhead, first-order accuracy, susceptible to instability.
, covering continuous and discrete event simulations, first-order differential equations, and collision detection. University of Calgary (CPSC 531) : Maintains a dedicated page for Systems Modeling and Simulation with downloadable course slides and tutorial materials. Professor Linda Friedman's Lectures : A specialized Google Site for Simulation Modeling Example: Monte Carlo structural stress analysis
: The simulation clock jumps directly to the timestamp of the next scheduled event in the Future Event List (FEL).
┌─────────────────┐ │ System Models │ └────────┬────────┘ │ ┌────────────────┴────────────────┐ ▼ ▼ ┌─────────────────────┐ ┌─────────────────────┐ │ Physical Models │ │ Mathematical Models │ └─────────────────────┘ └──────────┬──────────┘ │ ┌─────────────────────┴─────────────────────┐ ▼ ▼ ┌─────────────────────┐ ┌─────────────────────┐ │ Static vs Dynamic │ │Deterministic vs │ │ │ │Stochastic │ └─────────────────────┘ └─────────────────────┘ Static vs. Dynamic Models
: Statistical interpretation of output data to drive decisions. 2. Taxonomy of Simulation Models
: The long-term phase where system metrics stabilize around a true statistical mean. Handling Warm-Up Periods