Surrogate Modeling Applications for Storm-Surge Risk Predictions

Aug 16

Tuesday, August 16, 2022

TBA

Presenter

Alexandos A. Taflanidis, Professor, University of Notre Dame

https://duke.zoom.us/j/97283216160?pwd=TksvRlJoSStSSDhrNElvZzRFcFI2dz09
Meeting ID: 972 8321 6160
Passcode: 274932
ABSTRACT:
Numerical advances in storm surge prediction over the past couple of decades have produced high-fidelity simulation models that permit a detailed representation of hydrodynamic processes and therefore support high-accuracy forecasting. Unfortunately, the computational burden of such numerical models is large, requiring thousands of CPU hours for each simulation, something that limits their applicability for hurricane risk assessment. During landfalling events such models can be utilized to provide only a small number of high-fidelity, deterministic predictions, but cannot support thousand-run ensembles to examine the impact of forecasting errors in the predicted track or provide fast prediction updates once new storm track information becomes available...

Contact

Carpenter, Ruby Nell
660-5200
rubync@duke.edu