Improving Nonlinear PDE-Based Forecasts: Leveraging Conserved Quantities, Observational Data, and Machine Learning.

Simple item page
dc.contributor.advisorMohammad Farazmand, Chair
dc.contributor.advisorAlen Alexanderian, Member
dc.contributor.advisorHangjie Ji, Member
dc.contributor.advisorChi-An Yeh, Member
dc.contributor.authorHilliard, Zachary Thomas
dc.date.accepted2025-07-08
dc.date.accessioned2025-07-09T12:30:48Z
dc.date.available2025-07-09T12:30:48Z
dc.date.defense2025-06-20
dc.date.issued2025-06-20
dc.date.released2025-07-09
dc.date.reviewed2025-07-01
dc.date.submitted2025-06-25
dc.degree.disciplineApplied Mathematics
dc.degree.leveldissertation
dc.degree.nameDoctor of Philosophy
dc.descriptionNorth Carolina State University Theses Applied Mathematics.
dc.formatPh.D. North Carolina State University, 2025.
dc.identifier.otherdeg42950
dc.identifier.urihttps://www.lib.ncsu.edu/resolver/1840.20/45482
dc.titleImproving Nonlinear PDE-Based Forecasts: Leveraging Conserved Quantities, Observational Data, and Machine Learning.
dcterms.extent1 online resource (xi, 116 pages) : illustrations (some color)

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
etd.pdf
Size:
9.12 MB
Format:
Adobe Portable Document Format
Download

Collections

Dissertations