Combining Data-driven and Theory-guided Models in Ensemble Data Assimilation
| dc.contributor.author | Popov, Andrey Anatoliyevich | en |
| dc.contributor.committeechair | Sandu, Adrian | en |
| dc.contributor.committeemember | Iliescu, Traian | en |
| dc.contributor.committeemember | Karpatne, Anuj | en |
| dc.contributor.committeemember | Onufriev, Alexey | en |
| dc.contributor.committeemember | Evensen, Geir | en |
| dc.contributor.department | Computer Science and Applications | en |
| dc.date.accessioned | 2022-08-24T08:00:20Z | en |
| dc.date.available | 2022-08-24T08:00:20Z | en |
| dc.date.issued | 2022-08-23 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | As an illustrative example take the problem of predicting the weather. Typically a supercomputer will run a model several times to generate predictions few days into the future. Sensors such as those on satellites will then pick up observations about a few points on the globe, that are not representative of the whole atmosphere. These observations are combined, ``assimilated'' with the computer model predictions to create a better representation of our current understanding of the state of the earth. This predict-assimilate cycle is repeated every day, and is called (sequential) data assimilation. The prediction step traditional was performed by a computer model that was based on rigorous mathematics. With the advent of big-data, many have wondered if models based purely on data would take over. This has not happened. This thesis is concerned with taking traditional mathematical models and running them alongside data-driven models in the prediction step, then building a theory in which both can be used in data assimilation at the same time in order to not have a drop in accuracy and have a decrease in computational cost. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:35404 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/111608 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | ensemble data assimilation | en |
| dc.subject | data-driven modeling | en |
| dc.subject | multifidelity | en |
| dc.subject | covariance shrinkage | en |
| dc.title | Combining Data-driven and Theory-guided Models in Ensemble Data Assimilation | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |