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Modeling and simulation (M&S) is the use of models (e.g., physical, mathematical, behavioral, or logical representation of a system, entity, phenomenon, or process) as a basis for simulations to develop data utilized for managerial or technical decision making. [1] [2]
The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in applied mathematics and in the natural sciences (such as physics , biology , earth science , chemistry ) and engineering disciplines (such as computer science , electrical engineering ), as well as in non-physical systems such as the ...
The implementation of a multiphysics simulation follows a typical series of steps: [1] Identify the aspects of the system to be simulated, including physical processes, starting conditions, and the coupling or boundary conditions among these processes. Create a discrete mathematical model of the system. Numerically solve the model.
Modelling is the process of generating a model as a conceptual representation of some phenomenon. Typically a model will deal with only some aspects of the phenomenon in question, and two models of the same phenomenon may be essentially different—that is to say, that the differences between them comprise more than just a simple renaming of ...
Process simulation is a model-based representation of chemical, physical, biological, and other technical processes and unit operations in software. Basic prerequisites for the model are chemical and physical properties [ 1 ] of pure components and mixtures, of reactions, and of mathematical models which, in combination, allow the calculation ...
These models can also be seen as the evolution of the law of the random states of a nonlinear Markov chain. [10] [11] A natural way to simulate these sophisticated nonlinear Markov processes is to sample multiple copies of the process, replacing in the evolution equation the unknown distributions of the random states by the sampled empirical ...
Developing new process calculi for better modeling of computational phenomena. Finding well-behaved subcalculi of a given process calculus. This is valuable because (1) most calculi are fairly wild in the sense that they are rather general and not much can be said about arbitrary processes; and (2) computational applications rarely exhaust the ...
Given some experimental measurements of a system and some computer simulation results from its mathematical model, inverse uncertainty quantification estimates the discrepancy between the experiment and the mathematical model (which is called bias correction), and estimates the values of unknown parameters in the model if there are any (which ...