Date of Award:
8-2023
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Committee Chair(s)
Steve Petruzza
Committee
Steve Petruzza
Committee
John Edwards
Committee
Shuhan Yuan
Abstract
Particle tracing is a very important method for scientific visualization of vector fields, but it is computationally expensive. Deep learning can be used to speed up particle tracing, but existing deep learning models are domain-specific. In this work, we present a methodology to generalize the use of deep learning for particle tracing using transfer learning. We demonstrate the performance of our approach through a series of experimental studies that address the most common simulation design scenarios: varying time span, Reynolds number, and problem geometry. The results show that our methodology can be effectively used to generalize and accelerate the training and practical use of deep learning models for visualization of unsteady flows.
Checksum
73b03c9ae44d111b59a841b741ea2194
Recommended Citation
Gupta, Shubham, "Generalizing Deep Learning Methods for Particle Tracing Using Transfer Learning" (2023). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 8908.
https://digitalcommons.usu.edu/etd/8908
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