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DTSTART;TZID=America/Los_Angeles:20260817T100000
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DTSTAMP:20260810T162940Z
CREATED:20260810T162940Z
LAST-MODIFIED:20260810T162940Z
UID:10015328-1786960800-1786968000@live-events-ucsc.pantheonsite.io
SUMMARY:Nikolakakis\, M. (ECE) - Learned Gridless Representations of Cone Beam Computed Tomography Scans
DESCRIPTION:Medical image representation has long been dominated by voxel-grid matrices. While\ntheir inherent structure and order work efficiently for various linear transformations and\nprovide a seamless visualization method on monitors\, they fail to preserve the topology\nof the scan and to encode sparse information in a memory-efficient way.   The recent emergence of machine learning-based continuous coordinate-based\nscene representations such as neural radiance fields and Gaussian splatting has provided alternative representation techniques. These approaches overfit the weights of\na model by iterative differentiable rendering and have been shown to be more compact than grid representations. They are then able to perform novel view\nsynthesis from any given camera pose.\nOff-grid representations translate directly to Cone Beam Computed Tomography\nsparse-view acquisitions\, where streaking and quantum noise artifacts are dominant.\nUsing differentiable rendering\, a continuous representation is achieved\, with interpolation providing a path to recover some of the lost signal.\nIn this dissertation\, we apply a variety of methodologies\, including Gaussian splatting\, implicit occupancy fields\, and Neural Attenuation Fields regularized with an\nanatomic prior\, to Cone Beam Computed Tomography reconstruction\, and evaluate\ntheir performance across a range of anatomic datasets. Our models show that learned\ngridless representations achieve substantial memory reduction\, recover signal under\nextreme view sparsity\, and preserve scene topology. \nEvent Host: Manolis Nikolakakis\, Ph.D. Candidate\, Electrical and Computer Engineering  \nAdvisor: Razvan Marinescu \nZoom: https://ucsc.zoom.us/j/5964517596?pwd=c1AwRlJLNk5pVzFBUENibEw3by85Zz09
URL:https://live-events-ucsc.pantheonsite.io/event/nikolakakis-m-ece-learned-gridless-representations-of-cone-beam-computed-tomography-scans/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260818T100000
DTEND;TZID=America/Los_Angeles:20260818T110000
DTSTAMP:20260810T162245Z
CREATED:20260810T162245Z
LAST-MODIFIED:20260810T162245Z
UID:10015327-1787047200-1787050800@live-events-ucsc.pantheonsite.io
SUMMARY:Gutie\, J. (SciCAM) -  SORh: Hyperbolic Relaxation Methods For Elliptic Problems In Computational Fluid Dynamics
DESCRIPTION:This thesis explores iterative methods for solving elliptic partial differential equations (PDEs)\, which are used in computational fluid dynamics (CFD) to model a wide range of physical phenomena. The primary application of interest here is self-gravity\, modeled by Poisson’s equation. Although many numerical approaches exist\, including direct matrix inversion\, FFT-based methods\, and classical iterative methods such as Jacobi and Gauss-Seidel\, these approaches involve tradeoffs in computational cost\, scalability\, implementation complexity\, and adaptability to changing boundary conditions and problem configurations. \nTherefore\, we introduce SORh\, a simple and efficient relaxation method derived from a hyperbolic reformulation of Poisson’s equation. SORh generalizes classical successive over-relaxation (SOR) by providing independent control of residual relaxation and the directional propagation of Gauss–Seidel corrections. We present formulations of SORh in one and two spatial dimensions and investigate its stability\, accuracy\, and computational performance through analytical derivations and numerical comparisons with established relaxation methods. The results identify favorable SORh formulations\, clarify their relationships to classical relaxation methods\, and demonstrate improved convergence on selected test problems. Finally\, we demonstrate applications of SORh to astrophysical self-gravity simulations in the FLASH code and to magnetohydrodynamic (MHD) divergence cleaning. \nEvent Host: Jonathan Guite\, M.S. Candidate\, Scientific Computing & Applied Mathematics  \nAdvisor: Dongwook Lee \nZoom: https://ucsc.zoom.us/j/92153750104?pwd=ZdLiDZeLqOAlVNX9C4bCloKno9tAeB.1 \nPasscode: 769232
URL:https://live-events-ucsc.pantheonsite.io/event/gutie-j-scicam-sorh-hyperbolic-relaxation-methods-for-elliptic-problems-in-computational-fluid-dynamics/
CATEGORIES:Ph.D. Presentations
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