Adaptive computing for optimizing high-fidelity simulation runtimes

Domantay, Janelle

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https://hdl.handle.net/2142/127235 Copy

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

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.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127235

Copyright and License Information

Copyright 2024 Janelle Domantay

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