Scientific Domain 3:
Quantitative Modeling of Complex Physical Systems
The nature of physical systems is highly complex due to the non-linear interplay of multiple physical processes. Typically, each process lives on its own time scale and spatial scale. However, the quantitative prediction of the evolution of the full physical system requires the simultaneous and accurate modeling of all processes and their mutual interaction. Often the experimental exploration of the system is not feasible because it might be too costly, too time-consuming, too dangerous, or even impossible, like e.g. in many astrophysical problems.
Thus, the quantitative modeling of physical systems by means of large-scale computer simulations is nowadays essential in all areas of natural sciences. The numerical simulation of each process requires a clever algorithm, tuned to efficiently solve the problem-specific governing equations, which define the physical model. In combination with a set of input parameters, simulations are used to systematically investigate the evolution and the end state of the system. The output of the simulations is then validated, typically requiring and using tools which can handle and digest the resulting big data. This analysis is used to refine the underlying physical model and/or the significant parameter space. A successful application of this principle leads to a feedback loop that enables a continuous improvement of the quantitative predictive power of the physical model. This is the core of Scientific Domain 3: Quantitative Modeling of Complex Physical Systems.