{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/145140"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/145140","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Supervised Calibration and Uncertainty Quantification of Subgrid Closure Parameters using Ensemble Kalman Inversion","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_html":"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_has_math":false,"creators":["Hillier, Adeline"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Ferrari, Raffaele"],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:21:21Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/145140","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ferrari, Raffaele"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Supervised Calibration and Uncertainty Quantification of Subgrid Closure Parameters using Ensemble Kalman Inversion"]}]}],"canonical_facts":{"dc:contributor.advisor":["Ferrari, Raffaele"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Hillier, Adeline"],"dc:date.accessioned":["2022-08-29T16:36:02Z"],"dc:date.available":["2022-08-29T16:36:02Z"],"dc:date.issued":["2022-05"],"dc:description.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."],"dc:description.degree":["M.Eng."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/145140"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Supervised Calibration and Uncertainty Quantification of Subgrid Closure Parameters using Ensemble Kalman Inversion"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Engineering in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:21Z"}