Adaptive computing for optimizing high-fidelity simulation runtimes
Domantay, Janelle
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https://hdl.handle.net/2142/127235
Description
Title
Adaptive computing for optimizing high-fidelity simulation runtimes
Author(s)
Domantay, Janelle
Issue Date
2024-12-04
Director of Research (if dissertation) or Advisor (if thesis)
Driggs-Campbell, Katherine
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Energy Modeling
Adaptive Computing
Simulation
Sustainability
Optimization
Language
eng
Abstract
Physics-based and high-fidelity simulations are often leveraged to predict real-world trends and optimize resource consumption. However, these simulations are often computationally expensive and time consuming. Alternatively, small scale simulations can be performed at a fraction of the cost, but generate a host of scalability issues when translated to large-scale applications. To address model scalability issues, it is necessary to identify cost efficient methods for running physics-based models.Here we demonstrate how adaptive computing can be leveraged to create surrogate models that accurately approximate high-fidelity simulation behavior at a reduced runtime. We introduce a pipeline for training surrogate models that reaffirms the effectiveness of low-fidelity simulations. We elaborate on this pipeline for multi-scale simulations which demonstrate how adaptive computing can be used to stagger high-fidelity queries during surrogate training.These implementations demonstrate how adaptive computing can be used to manipulate model run-time and increase accuracy with reduced computational budgets.These implementations can be leveraged in any existing modeling pipeline regardless of discipline.
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