May 1, 2024
Updated November 11, 2026
24 minute read
Simulation is a powerful technique that involves creating and analyzing a digital prototype of a physical model to predict its performance in the real world. It allows us to explore "what-if" scenarios, test different designs, and understand complex systems without the cost, time, or risk associated with building and testing physical counterparts. From designing the next generation of aircraft to predicting the spread of diseases or optimizing financial portfolios, simulation provides a virtual laboratory for innovation and decision-making across a vast array of fields.
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Find a path to becoming a Simulation. Learn more at:
OpenCourser.com/topic/196d9w/simulatio
Reading list
We've selected 17 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Simulation.
Stands as the undisputed gold-standard reference in discrete-event simulation, covering input probability distribution fitting, random-number generation, model verification and validation, output statistical analysis, and variance-reduction experiments across industrial and computing systems.
Teaches hands-on computational modeling from the ground up using Python notebooks, walking learners through discrete time-step models, queuing systems, epidemiological compartments, first-order differential equations, and physical mechanics with clear, iterative code.
Focuses on the probabilistic and algorithmic foundations of Monte Carlo simulation, Markov Chain Monte Carlo (MCMC), Poisson processes, bootstrap resampling, and importance sampling/control variates for quantitative analysts and computer scientists.
Models complex adaptive systems from the bottom up using NetLogo, demonstrating how simple local interaction rules among autonomous agents generate emergent dynamics across ecology, traffic flows, epidemiology, and computational social science.
Walks industrial engineers and operations analysts through building process-oriented discrete-event simulations, resource schedules, entity routing, and steady-state statistical experiments for manufacturing lines, supply chains, and service operations.
Provides a comprehensive university curriculum on event-list scheduling algorithms, queueing network theory, random-variate generation, and confidence-interval estimation for comparing alternative system designs under stochastic uncertainty.
Establishes the formal Discrete Event System Specification (DEVS) mathematical hierarchy that unifies continuous differential equations, discrete-event automata, and hybrid co-simulation architectures in complex systems engineering.
Bridges classroom theory and real-world consulting by structuring the entire lifecycle of a simulation project—from problem formulation and field data collection to verification walkthroughs, design of experiments, and executive presentation.
Illustrates real-time continuous-time simulation engineering through six-degree-of-freedom equations of motion, numerical integration stability, aerodynamic lookup tables, navigation instrumentation, and real-time visual rendering loops.
Introduces system dynamics, cellular automata, Monte Carlo methods, and agent-based simulations across biology, physics, and computer science, helping students translate real-world rate equations and stochastic transitions into executable models.
Demonstrates how physics-based particle simulations integrate Newton's equations of motion across interatomic potentials, thermostats, and boundary conditions to predict material mechanics and nanoscale dynamics.
Walks data practitioners through implementing Monte Carlo financial simulations, SimPy discrete-event queues, Markov chains, and cellular automata in Python to optimize real-world industrial and business operations.
Masterwork on stochastic simulation of sample paths, Brownian bridges, quasi-Monte Carlo low-discrepancy sequences, and variance-reduction techniques widely applied across quantitative finance and risk engineering.
Equips Python engineers and analysts to replace costly proprietary tools with SimPy, stepping from basic process generators and service queues to priority resources, Monte Carlo runs, and full factorial experimental analysis.
Connects live cyber-physical data architectures with scenario simulation to enable risk-free what-if experimentation, detailing digital twin methodologies across manufacturing, logistics networks, supply chains, and healthcare systems.
Distills decades of MIT system dynamics simulation research into an unforgettable conceptual guide on stocks, flows, delays, reinforcing feedback loops, and leverage points in complex ecological, economic, and organizational systems.
Synthesizes discrete-event simulation, agent-based modeling, and system dynamics into unified multi-method frameworks, illustrating how combining simulation paradigms with machine learning solves complex socio-technical problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/196d9w/simulatio
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