Modelling simulation and optimization

Modelling and Simulation is the use of models – physical, mathematical, or otherwise logical representation of a system, entity, phenomenon, or process – as a basis for simulations – methods for implementing a model over time – to develop data as a basis for managerial or technical decision making. Using simulations is generally cheaper, safer and sometimes more ethical than conducting real-world experiments. Simulation-based optimization integrates optimization techniques into simulation analysis. Because of the complexity of the simulation, the objective function may become difficult and expensive to evaluate.

Once a system is mathematically modelled, computer-based simulations provide information about its behavior. In physics-related problems, Monte Carlo methods are useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures. Agent-based modelling is related to, but distinct from, the concept of multi-agent systems or multi-agent simulation in that the goal of ABM is to search for explanatory insight into the collective behavior of agents obeying simple rules, typically in natural systems, rather than in designing agents or solving specific practical or engineering problems.

 

  • Simulation
  • Agent-based model
  • Monte Carlo method
  • Individual-Based Models
  • Simulation-based optimization
  • Uncertainty Quantification

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