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Predicting the Behavior of Large Dynamical Systems using Intervals and Reduced-Order Modeling
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
We do this using interval computations and constraint solving techniques.
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