Date of Award

Fall 2024

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

Program/Concentration

Computer Science

Committee Director

Yaohang Li

Committee Member

Ravi Mukkamala

Committee Member

Douglas Adams

Abstract

Symbolic Regression (SR) is a cutting-edge machine learning technique that discovers mathematical expressions representing the underlying patterns in data. Unlike traditional regression, SR explores a wide range of mathematical models, allowing for flexible and interpretable solutions. We utilize PySR, a highly customizable symbolic regression package, which combines genetic programming and modern optimization methods to efficiently search for interpretable equations. PySR balances model complexity with performance by penalizing overly complex expressions while optimizing accuracy. In this thesis, we apply Physics-informed Symbolic Regression through PySR to model the x and t dependence of the flavor isovector combination Hu − d(x,t,ζ ,Q2 ) at ζ = 0 and Q2 = 4GeV2 . Our PySR models are trained on Generalized Parton Distribution (GPD) pseudo data, sourced from both Lattice QCD and contemporary models such as GGL. By incorporating physics constraints into the symbolic regression process, our models satisfy physical principles relevant to GPDs while achieving low mean-squared error. A custom loss function was employed, encouraging models that both fit the data well and adhered to physical constraints. We compare the performance of PySR-derived GPDs against traditional Regge parameterizations and Neural Network-based GPDs.This research demonstrates the power of PySR for extracting interpretable expressions from complex physics data, offering a novel tool for scientific modeling that merges data-driven techniques with domain-specific knowledge.

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In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).

DOI

10.25777/g4en-3x11

ISBN

9798302855473

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