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|CSDMS meeting abstract presentation=Many geophysical models require parameters that are not tightly constrained by
|CSDMS meeting abstract presentation=Many geophysical models require parameters that are not tightly constrained by observational data. Calibration represents methods by which these parameters are estimated by minimizing the difference between observational data and model simulated equivalents (the objective function). Additionally, uncertainty in estimated parameters is determined.


observational data. Calibration represents methods by which these parameters
In this clinic we will cover the basics of model calibration including: (1) determining an appropriate objective function, (2) major classes of calibration algorithms, (3) interpretation of results.  


are estimated by minimizing the difference between observational data and
In the hands-on portion of the the clinic, we will apply multiple calibration algorithms to a simple test case. For this, we will use Dakota, a package that supports the application of many different calibration algorithms.
 
model simulated equivalents (the objective function). Additionally, uncertainty
 
in estimated parameters is determined.
 
 
In this clinic we will cover the basics of model calibration including:
 
(1) determining an appropriate objective function, (2) major classes of
 
calibration algorithms, (3) interpretation of results.
 
 
In the hands-on portion of the the clinic, we will apply multiple calibration  
 
algorithms to a simple test case. For this, we will use Dakota, a package that  
 
supports the application of many different calibration algorithms.
|CSDMS meeting youtube code=0
|CSDMS meeting youtube code=0
|CSDMS meeting participants=0
|CSDMS meeting participants=0

Revision as of 12:36, 8 January 2019

CSDMS3.0 - Bridging Boundaries


Model Calibration with Dakota



Barnhart Katy

University of Colorado, Boulder, United States
katy.barnhart@gmail.com


Abstract
Many geophysical models require parameters that are not tightly constrained by observational data. Calibration represents methods by which these parameters are estimated by minimizing the difference between observational data and model simulated equivalents (the objective function). Additionally, uncertainty in estimated parameters is determined.

In this clinic we will cover the basics of model calibration including: (1) determining an appropriate objective function, (2) major classes of calibration algorithms, (3) interpretation of results.

In the hands-on portion of the the clinic, we will apply multiple calibration algorithms to a simple test case. For this, we will use Dakota, a package that supports the application of many different calibration algorithms.



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Of interest for:
  • Terrestrial Working Group
  • Coastal Working Group
  • Marine Working Group
  • Education and Knowledge Transfer (EKT) Working Group
  • Cyberinformatics and Numerics Working Group
  • Hydrology Focus Research Group
  • Carbonates and Biogenics Focus Research Group
  • Chesapeake Focus Research Group
  • Critical Zone Focus Research Group
  • Human Dimensions Focus Research Group
  • Geodynamics Focus Research Group
  • Ecosystem Dynamics Focus Research Group
  • Coastal Vulnerability Initiative
  • Continental Margin Initiative
  • Artificial Intelligence & Machine Learning Initiative