Speaker:	Dr. Valeriy Ivanov
Institution:	Department of Civil & Environmental Engineering
	University of Michigan, Ann Arbor
Seminar Date:	Thursday, May 20th, 2021, 3:30-4:50 PM

Flooding impacts are on the rise globally, and concentrated in urban areas. Currently, there are no operational systems to forecast flooding at “human scales” to facilitate emergency preparedness and response actions that mitigate flood impacts. We present a framework for real time flood modeling and uncertainty quantification that combines the physics of fluid motion with advances in applied mathematics. The framework overcomes the prohibitive computational demands of high-fidelity modeling in real-time by using a probabilistic learning method relying on surrogate models that are trained prior to a flood event. This shifts the overwhelming burden of computation to the trivial problem of data storage, and enables forecasting of both flood hazard and its uncertainty at scales that are vital for time-critical decision-making before and during extreme events. The framework has the potential to improve flood prediction and analysis and can be extended to other hazard assessments requiring intense high-fidelity computations in real-time.