Combining Data-driven and Theory-guided Models in Ensemble Data Assimilation

There once was a dream that data-driven models would replace their theory-guided counterparts. We have awoken from this dream. We now know that data cannot replace theory. Data-driven models still have their advantages, mainly in computational efficiency but also providing us with some special sauce that is unreachable by our current theories. This dissertation aims to provide a way in which both the accuracy of theory-guided models, and the computational efficiency of data-driven models can be combined. This combination of theory-guided and data-driven allows us to combine ideas from a much broader set of disciplines, and can help pave the way for robust and fast methods.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Popov, Andrey Anatoliyevich

Chair dc:contributor.committeechair

• Sandu, Adrian

Committee members dc:contributor.committeemember

• Iliescu, Traian
• Karpatne, Anuj
• Onufriev, Alexey
• Evensen, Geir

• ensemble data assimilation
• data-driven modeling
• multifidelity
• covariance shrinkage

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35404

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/111608