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1
Introduction
2
Lab overview
3
Tribology
4
Naval and Sim Center
5
Open Source Framework
6
Tsunami 2004
7
Tsunami 2010
8
Tsunami 2011
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Storm Surge
10
Tsunami
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Modeling approaches
12
Mechanics course
13
Ocean floor
14
Hydrouq
15
Depth Average
16
Boundary Conditions
17
Depth
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Steady State
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Discretization
20
Example
21
Propagation of uncertainties
22
Engineering judgment
23
Forward propagation
24
Reliability analysis
25
Global sensitivity analysis
26
Sur surrogate models
27
Inverse UQ
28
Questions
29
QA Session
Description:
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.

Uncertainty Quantification and Deep Learning for Water-Hazard Prediction

Inside Livermore Lab
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