digitalsimulation

Digital simulation is the use of computer-based models to imitate the behavior of real-world systems, processes, or events over time. It encompasses approaches such as discrete-event simulation, continuous simulation, agent-based modeling, and system dynamics. A digital simulation represents a system as a set of entities, state variables, and rules that govern transitions, and it runs experiments by advancing time to observe outputs under different scenarios.

Models are built from mathematical equations, empirical data, and domain knowledge. Simulation software or general programming languages implement the models, perform numerical integration or event scheduling, and produce results such as performance metrics, distributions, and visualizations. Stochastic techniques, including Monte Carlo simulation, are used to quantify uncertainty by repeating trials with varied inputs.

Applications span manufacturing and logistics for capacity planning and process optimization; engineering for design validation and reliability assessment; urban planning and transportation to evaluate policies; energy and environmental modeling; healthcare for patient flow and treatment planning; finance for risk assessment and pricing; and education and research.

Historically, digital simulation developed in the mid-20th century with early stochastic and deterministic models, followed by dedicated simulation languages and toolkits in the 1960s–1990s, and expanded with modern programming environments and high-performance computing.

Digital simulation is closely related to the concept of a digital twin, where a live, data-connected model mirrors a physical asset or system. The twin often integrates real-time sensor data to run near-real-time simulations for monitoring and decision support.

Key challenges include ensuring model validity and data quality, computational requirements, and managing uncertainty, bias, and interpretability. Proper verification, validation, and documentation are essential for credible results.