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{{Presenters presentation
{{Presenters presentation
|CSDMS meeting abstract presentation=Numerical modeling is at the core of prediction in coastal settings. Observational data is used in tandem with models for a variety of modeling tasks, but the perhaps the coupling could be tighter? I will discuss a range of Machine Learning tools that co-workers and I have integrated with coastal morphodynamic models that allow for a tight coupling of models and data, and provide morphodynamic insight.
|CSDMS meeting abstract presentation=Numerical modeling is at the core of prediction in coastal settings. Observational data is used in tandem with models for a variety of modeling tasks, but the perhaps the coupling could be tighter? I will discuss a range of Machine Learning tools that co-workers and I have integrated with coastal morphodynamic models that allow for a tight coupling of models and data, and provide morphodynamic insight.
|CSDMS meeting youtube code=0
|CSDMS meeting youtube code=3RdlIHZsZKE
|CSDMS meeting participants=0
|CSDMS meeting participants=0
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Latest revision as of 06:27, 27 May 2019

CSDMS3.0 - Bridging Boundaries


Machine Learning and Coastal Morphodynamics



Evan Goldstein

University of North Carolina at Greensboro, United States
ebgoldstein@gmail.com


Abstract
Numerical modeling is at the core of prediction in coastal settings. Observational data is used in tandem with models for a variety of modeling tasks, but the perhaps the coupling could be tighter? I will discuss a range of Machine Learning tools that co-workers and I have integrated with coastal morphodynamic models that allow for a tight coupling of models and data, and provide morphodynamic insight.

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Of interest for:
  • Coastal Working Group
  • Artificial Intelligence & Machine Learning Initiative