Uncertainty Quantification and Deep Learning for Water-Hazard Prediction

Explore uncertainty quantification and deep learning techniques for predicting water hazards in this comprehensive lecture. Delve into the complexities of modeling storm surge events and their impact on urban infrastructure, including the challenges of quantifying uncertainties in flow conditions and structural properties. Learn about innovative approaches combining Bayesian calibration and neural networks to characterize and assess damage from extreme weather events. Discover recent developments in the field as Dr. Ajay B Harish, a lecturer in Engineering Simulation and Data Science, shares insights on modeling water-borne hazards like storm surges and tsunamis. Gain valuable knowledge on numerical methods, data-driven physical simulations, and their applications in enhancing disaster preparedness and response strategies.