Keywords: Simulation, FDA, Design of Experiments, Gaussian-Process Emulators, Sensitivity Analysis, Uncertainty Propagation
Advancements in simulation software and high-performance computing have led to the possibility of new technologies like the Digital Twin which deliver a continuous stream of complex data from simulation models. However, analyses of these data streams using basic statistical methods do not always yield the results sought. The emergence of complex data from these technologies has spawned several issues for extracting pertinent and meaningful information. Companies in many industries using modeling and simulation have found dramatic reduction in costs and development times for new technologies and products as well as increased product reliability and decreased risk of unexpected failures. The benefits of modeling and simulation have been recognized at a policy level and requirements for the application of robust uncertainty quantification (UQ) in modeling and simulation are beginning to be adopted by regulatory agencies including the Federal Aviation Agency (FAA) and Food and Drug Administration(FDA).
Industry is also becoming more aware to the potential cost savings and reduction of risk offered by adopting modeling and simulation. We will discuss questions such as:
• What are the current issues in the analysis of simulation and modeling? • What current statistical methods can help improve this analysis? • What does the future hold for applying statistical methods to simulation and modeling practices?