Presenters-0426: Difference between revisions
From CSDMS
No edit summary |
m Text replacement - "\|CSDMS meeting youtube views=\{\{(Youtube_[^}]+)\}\}" to "|CSDMS meeting youtube views={{#explode:{{$1}}| |0}} |CSDMS meeting youtube AverageViews={{#explode:{{$1}}| |1}}" |
||
| (4 intermediate revisions by 3 users not shown) | |||
| Line 3: | Line 3: | ||
|CSDMS meeting event year=2019 | |CSDMS meeting event year=2019 | ||
|CSDMS meeting presentation type=Clinic | |CSDMS meeting presentation type=Clinic | ||
|CSDMS meeting first name= | |CSDMS meeting first name=Katy | ||
|CSDMS meeting last name= | |CSDMS meeting last name=Barnhart | ||
|CSDMS meeting institute=University of Colorado, Boulder | |CSDMS meeting institute=University of Colorado, Boulder | ||
|Country member=United States | |Country member=United States | ||
| Line 12: | Line 12: | ||
}} | }} | ||
{{Presenters presentation | {{Presenters presentation | ||
|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. | |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. | ||
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 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. | 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 youtube views={{#explode:{{Youtube_0}}| |0}} | |||
|CSDMS meeting youtube AverageViews={{#explode:{{Youtube_0}}| |1}} | |||
|CSDMS meeting participants=0 | |CSDMS meeting participants=0 | ||
}} | }} | ||
| Line 27: | Line 29: | ||
}} | }} | ||
{{Presenters additional material | {{Presenters additional material | ||
|Working group member=Terrestrial Working Group, Coastal Working Group, Marine Working Group, Education and Knowledge Transfer (EKT) Working Group, Cyberinformatics and Numerics Working Group, Hydrology | |Working group member=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, 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 | ||
}} | }} | ||
Latest revision as of 16:34, 11 June 2025
CSDMS3.0 - Bridging Boundaries
Model Calibration with Dakota
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.
Please acknowledge the original contributors when you are using this material. If there are any copyright issues, please let us know (CSDMSweb@colorado.edu) and we will respond as soon as possible.
Of interest for:
