Difference between revisions of "Using Gaussian Stochastic Processes (GaSP) for Hazard Mapping"

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'''Student Researchers:''' [[User:John.Bihn|John Bihn]], [[User:Tao.Cui|Tao Cui]], and [[User:Dakota.Sullivan|Dakota Sullivan]]
 
 
 
'''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
 
'''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 storm surge during hurricanes and tropical storms.
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'''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.
 
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__TOC__
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== Milestones ==
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=== Week 1 (6/1 - 6/5) ===
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* Read about statistical surrogates
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* Plot test function
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* Find optimization routine in Python
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* Become comfortable with LaTeX
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=== Week 2 (6/8 - 6/12) ===
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* Complete reading on storm surges
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** Technical paper from Dr. Spiller
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** Non-technical, independent research
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* Use optimization routine to find parameters to fit
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=== Week 3 (6/15 - 6/19) ===
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* Play around with reading data files from computer experiment
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** Hopefully storm surge data – maybe move to Week 4
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* Implement optimization of model posterior (∴)
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=== Week 4 (6/22 – 6/26) ===
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* Continue on (∴)
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* Play around with reading data files and plotting outputs
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* Reading about Monte Carlo calculations
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* Regroup and discuss plan for July
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=== Week 5 (6/29 – 7/3) ===
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* '''Mini-presentation on July 2nd'''
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* Work on applying surrogate to storm surge model (♦)
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* Identify challenges in doing so
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=== Week 6 (7/6 – 7/10) ===
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* Continue to work on (♦)
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* Use surrogate in probability calculation (∗)
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* Discuss possible scenario model
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=== Week 7 (7/13 – 7/17) ===
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* Work on (♦) and (∗)
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=== Week 8 (7/20 – 7/24) ===
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* Work on (♦) and (∗)
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=== Week 9 (7/27 – 7/30) ===
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* '''Posters due July 29th'''
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=== Week 10 (8/3 – 8/7) ===
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'''Previous Student Researchers:''' [[User:John.Bihn|John Bihn]], [[User:Tao.Cui|Tao Cui]], and [[User:Dakota.Sullivan|Dakota Sullivan]]
* '''Finish presentations and papers'''
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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