Predicting the Behavior of Large Dynamical Systems using Intervals and Reduced-Order Modeling

09/25/2017
Speaker(s) : Martine Ceberio (University of Texas at El Paso)
Abstract: The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, uncertainty is hardly taken into account. Changes in the definition of a model, for instance, could have dramatic effects on the outcome of simulations.
In this research, we are interested in handling uncertainty and in expanding this ability to be able to analyze unfolding phenomena whose features are originally unknown. This is particularly important in providing understanding of developing situations and possibly in allowing for preventative or palliative measures before a situation aggravates.
We do this using interval computations and constraint solving techniques.