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Simulation-Based Global Optimization
1963 - 1994
During 1963 through 1994, Modeling and Simulation established itself as a unifying methodological framework across engineering, hydrology, traffic, and materials science, emphasizing robust numerical methods, simulation design, and experiment thinking. Stochastic optimization and simulated annealing emerged as core tools for solving hard engineering problems within simulation contexts, enabling probabilistic exploration of large design spaces and driving the broader metaheuristics movement. Advances in subgrid and turbulence modeling, multiscale approaches, and discrete-kinetic methods such as lattice-based schemes expanded the fidelity and efficiency of fluid and gas simulations. Engineering design optimization increasingly relied on design-of-experiments and heuristic control embedded in simulation workflows, linking theoretical models to practical engineering practice.
• Stochastic optimization and simulated annealing emerged as a core approach for solving hard engineering optimization problems, including TSP, parameter design, and scheduling in simulation contexts [1], [5].
• Simulation serves as a unifying methodological framework across domains like hydrology, reservoirs, grain growth, and traffic, emphasizing numerical methods and simulation design across disciplines [6], [7], [8], [10], [11], [20].
• Subgrid and turbulence modeling highlight multiscale approaches to flow phenomena, leveraging turbulence theory and subgrid procedures to improve simulations [7], [12], [19].
• Lattice-gas and Boltzmann-based discrete kinetic methods provide a computational framework for simulating fluids and gas dynamics [13], [14].
• Engineering design optimization and planning employ design-of-experiments and heuristic control within simulation contexts [9], [15], [18].
Integrated Complex Systems Modeling
1995 - 2001
End-to-End Multiscale Simulation
2002 - 2008
Standardized Modeling Lifecycle
2009 - 2015
Hybrid Data-Driven Modeling
2016 - 2024