Scientific discovery, or understanding, involves the formulation of theory to explain observed phenomena, the design and execution of experiments to test theory and the feedback of experimental results to evolve theory. This process is known as the scientific method and plays a central role in any scientific discovery endeavor. Modeling and simulation live at the intersection between theory and experiment. On the one hand a model and its associated implementation in software (e.g. a simulation tool) are an expression of theory. On the other hand, the execution of that software (e.g. running the simulation) and the collection of results from it can be viewed as a virtual experiment.
A fundamental question becomes, to what extent can the results from a simulated or virtual experiment be trusted? This question often arises even for actual experiments in the real-world too. But, it is doubly important and substantially more challenging to answer for simulated or virtual experiments. It involves validation, verification and uncertainty quantification (VV&UQ) of the simulation software as well as the data it consumes and produces.
In any case, modeling and simulation are never intended to replace actual experiments in the real-world. This is so important, it is worth emphasizing. Modeling and simulation should never be viewed as a substitute for real-world experimentation.
Instead, modeling and simulation are highly valued in scientific discovery because they provide additional insights that are often impractical or impossible to discover through real-world experimental and theoretical analysis alone. Modeling and simulation serve to compliment and inform the experimental and theoretical arms of the scientific method.
