2026-09-19T06:29:29Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1451402022-08-30T03:44:39Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Ferrari, Raffaele
Hillier, Adeline
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-08-29T16:36:02Z
2022-08-29T16:36:02Z
2022-05
2022-05-27T16:19:21.690Z
https://hdl.handle.net/1721.1/145140
Data-driven approaches are increasingly being used to identify and remove structural biases in dynamical models for real-world systems. However, because model updates alter the dependency of a model on its free parameters, evidence about structural biases is often muddied by the variable influences of inadequately-tuned parameters on the model solution. We elaborate a framework for model development that combines calibration, sensitivity analysis, and uncertainty quantification of free parameters to shed light on where structural biases are likely to exist in a model, and where the model may be unnecessarily complex. The approach is useful for general applications because it is easy to implement, derivative-free, robust against model instabilities, and computationally inexpensive, requiring a modest number of model evaluations. A diffusive closure for turbulence penetrated by air-sea fluxes of the ocean surface, presently called the “Convective Turbulent Kinetic Parameterization," is developed as a testbed for and proof-of-concept for the approach. Modifications to the traditional Ensemble Kalman Inversion [1] algorithm are devised to improve convergence during the calibration phase of this process. Further, the Calibrate Emulate Sample [2] framework for uncertainty quantification is validated with modifications.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Supervised Calibration and Uncertainty Quantification of Subgrid Closure Parameters using Ensemble Kalman Inversion
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="5632bf2f-e433-465e-8521-a7b43e32df2a">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Supervised Calibration and Uncertainty Quantification of Subgrid Closure Parameters using Ensemble Kalman Inversion</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2022-05</PublicationDate>
<Authors>
<Author>
<DisplayName>Hillier, Adeline</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>Data-driven approaches are increasingly being used to identify and remove structural biases in dynamical models for real-world systems. However, because model updates alter the dependency of a model on its free parameters, evidence about structural biases is often muddied by the variable influences of inadequately-tuned parameters on the model solution. We elaborate a framework for model development that combines calibration, sensitivity analysis, and uncertainty quantification of free parameters to shed light on where structural biases are likely to exist in a model, and where the model may be unnecessarily complex. The approach is useful for general applications because it is easy to implement, derivative-free, robust against model instabilities, and computationally inexpensive, requiring a modest number of model evaluations. A diffusive closure for turbulence penetrated by air-sea fluxes of the ocean surface, presently called the “Convective Turbulent Kinetic Parameterization," is developed as a testbed for and proof-of-concept for the approach. Modifications to the traditional Ensemble Kalman Inversion [1] algorithm are devised to improve convergence during the calibration phase of this process. Further, the Calibrate Emulate Sample [2] framework for uncertainty quantification is validated with modifications.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>