Difference between revisions of "Using Gaussian Stochastic Processes (GaSP) for Hazard Mapping"
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'''Mentor:''' [http://www.marquette.edu/mscs/facstaff-spiller.shtml Elaine Spiller] | '''Mentor:''' [http://www.marquette.edu/mscs/facstaff-spiller.shtml Elaine Spiller] | ||
| + | '''Approach:''' Develop and evaluate efficient statistical surrogates for computationally complex computer models | ||
| − | + | '''Summary:''' Hazard mapping is an essential tool used to estimate the risk faced by residents living in areas susceptible to natural disasters. However, accurate computer models are often computationally complex and can take hours or even days to compute. This limits the utility of computer models during storms, where conditions are frequently changing. An alternative is to create a statistical surrogate using Gaussian stochastic process (GaSP) models that can be computed more efficiently than a computer model. In this project, we hope to apply this method to model landslides, using data gathered from previously conducted simulations. | |
| − | + | '''Previous Student Researchers:''' [[User:John.Bihn|John Bihn]], [[User:Tao.Cui|Tao Cui]], and [[User:Dakota.Sullivan|Dakota Sullivan]] | |
Latest revision as of 02:27, 20 January 2017
Mentor: Elaine Spiller
Approach: Develop and evaluate efficient statistical surrogates for computationally complex computer models
Summary: Hazard mapping is an essential tool used to estimate the risk faced by residents living in areas susceptible to natural disasters. However, accurate computer models are often computationally complex and can take hours or even days to compute. This limits the utility of computer models during storms, where conditions are frequently changing. An alternative is to create a statistical surrogate using Gaussian stochastic process (GaSP) models that can be computed more efficiently than a computer model. In this project, we hope to apply this method to model landslides, using data gathered from previously conducted simulations.
Previous Student Researchers: John Bihn, Tao Cui, and Dakota Sullivan
