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DTSTART;TZID=America/Los_Angeles:20260817T010000
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SUMMARY:Graduate Preparation Program
DESCRIPTION:The Graduate Preparation Program (GPP) is a four-week intensive non-credit course offered in person on the main campus prior to the fall quarter\, which is open to all current and newly admitted international graduate students. It focuses on English for Academic Purposes (EAP)\, academic skills\, and cultural orientation. The program also provides a foundation for transitioning into the Teaching Assistant role. \nThe course offers guided support with: \n\nSeminar-style classroom discussion\nPresentation and oral communication skills\nInformation on U.S. classroom culture; roles and responsibilities of faculty\, Teaching Assistants\, and students\nCritical thinking: using evidence to support ideas\nResearch writing\, citation\, and avoiding plagiarism\nCollaborative and project-based learning\n\nA collaboration between the Graduate Division and Global Engagement\, the Graduate Preparation Program provides a strong foundation for the U.S. classroom environment and university system\, in addition to the opportunity to practice and master the language and academic skills needed for success. \nFor more information and to register\, please visit our webpage.
URL:https://events.ucsc.edu/event/graduate-preparation-program/
CATEGORIES:Conference,Lectures & Presentations
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DTSTART;TZID=America/Los_Angeles:20260818T100000
DTEND;TZID=America/Los_Angeles:20260818T110000
DTSTAMP:20260810T162245Z
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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://events.ucsc.edu/event/gutie-j-scicam-sorh-hyperbolic-relaxation-methods-for-elliptic-problems-in-computational-fluid-dynamics/
CATEGORIES:Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260818T140000
DTEND;TZID=America/Los_Angeles:20260818T160000
DTSTAMP:20260817T160201Z
CREATED:20260817T155423Z
LAST-MODIFIED:20260817T160201Z
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SUMMARY:Lupin-Jimenez\, L. (AM) - Data-Driven Deep Learning for Turbulent Phenomena: Regional Ocean Prediction and Assimilation\, Spectral Bias in Diffusion Models\, and Equation Discovery
DESCRIPTION:Deep learning models trained on simulation and reanalysis data can now emulate turbulent geophysical flows at a small fraction of the computational cost of numerical solvers.\nTheir scientific utility depends on physical consistency\, which for the systems studied here\nrests in large part on spectral fidelity\, the accurate reconstruction of variance across spatial\nscales. This document presents two published studies and two studies in progress that develop\, analyze\, and apply data-driven methods for turbulent phenomena along that thread.\nThe first study develops FCDS\, a framework that autoregressively emulates surface ocean\ndynamics over the Gulf of Mexico at 8 km resolution and simultaneously downscales and\nbias-corrects the emulated fields to 4 km\, with a spectral loss that keeps decadal integrations stable and statistically consistent with a high-resolution reanalysis. The second study\ndevelops a neural-operator-conditioned denoising diffusion model that reconstructs regional\nsurface ocean states from Lagrangian-like observations at 99% and 99.9% sparsity without a\nbackground dynamical model\, and shows that the recovered small-scale dynamics are visible\nin spectral diagnostics but not in pointwise metrics. The third study derives a signal-tonoise theory of spectral bias in diffusion models for 2D turbulence\, organized around the\ncrossover wavenumber kc(τ) at which signal and noise contribute equal power\, and validates\nits predictions on a sweep of 28 models spanning seven forcing wavenumbers and four noise\nschedulers. The fourth study develops a window-pair spectral method for discovering governing equations from single-point sensor measurements of soliton dynamics in a superfluid\nwave flume\, replacing noise-amplifying instantaneous derivatives with finite-time spectral\nshifts and verifying the discovered equations against a measured-scalar null model. A concluding chapter summarizes the results and outlines future work on novel architectures and\nmethods for data-driven emulation of physical simulations. \nEvent Host: Leonard Lupin-Jimenez\, Ph.D. Student\, Applied Mathematics  \nAdvisor: Ashesh Chattopadhyay \nZoom: https://ucsc.zoom.us/j/97866640488?pwd=UJdTs3sxKfFbz5mabKLIyx5ZYF90J9.1 \nPasscode: 815911
URL:https://events.ucsc.edu/event/lupin-jimenez-l-am-data-driven-deep-learning-for-turbulent-phenomena-regional-ocean-prediction-and-assimilation-spectral-bias-in-diffusion-models-and-equation-discovery/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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