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From neuro-inspired attention methods to generative diffusion: applications to weather and climate

dc.contributor.authorStock, Jason, author
dc.contributor.authorAnderson, Chuck, advisor
dc.contributor.authorEbert-Uphoff, Imme, committee member
dc.contributor.authorKrishnaswamy, Nikhil, committee member
dc.contributor.authorSreedharan, Sarath, committee member
dc.date.accessioned2024-12-23T12:00:23Z
dc.date.available2024-12-23T12:00:23Z
dc.date.issued2024
dc.description.abstractMachine learning presents new opportunities for addressing the complexities of atmospheric science, where high-dimensional, sparse, and variable data challenge traditional methods. This dissertation introduces a range of algorithms, motivated specifically by the intricacies of weather and climate applications. These challenges complement those that are fundamental in machine learning, such as extracting relevant features, generating high-quality imagery, and providing interpretable model predictions. To this end, we propose methods to integrate adaptive wavelets and spatial attention into neural networks, showing improvements on tasks with limited data. We design a memory-based model of sequential attention to expressively contextualize a subset of image regions. Additionally, we explore transformer models for image translation, with an emphasis on explainability, that overcome the limitations of convolutional networks. Lastly, we discover meaningful long-range dynamics in oscillatory data from an autoregressive generative diffusion model---a very different approach from the current physics-based models. These methods collectively improve predictive performance and deepen our understanding of both the underlying algorithmic and physical processes. The generality of most of these methods is demonstrated on synthetic data and classical vision tasks, but we place a particular emphasis on their impact in weather and climate modeling. Some notable examples include an application to estimate synthetic radar from satellite imagery, predicting the intensity of tropical cyclones, and modeling global climate variability from observational data for intraseasonal predictability. These approaches, however, are flexible and hold potential for adaptation across various application domains and data modalities.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierStock_colostate_0053A_18736.pdf
dc.identifier.urihttps://hdl.handle.net/10217/239893
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectcomputer vision
dc.subjectgenerative AI
dc.subjectsevere weather
dc.subjectdeep learning
dc.subjectclimate change
dc.subjectmachine learning
dc.titleFrom neuro-inspired attention methods to generative diffusion: applications to weather and climate
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineComputer Science
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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