Andrew Glaws

Andrew Glaws is a Researcher in Applied Mathematics at the National Renewable Energy Laboratory (NREL), working within the Computational Science Center. He joined NREL as a postdoctoral researcher in January 2019 and transitioned to a Researcher position in 2021, focusing on physics-informed deep learning for energy systems.

His educational background includes:

• PhD in Computer Science, University of Colorado Boulder
• Master of Science in Mathematics, Virginia Polytechnic Institute and State University
• Bachelor of Science in Mathematics, Vanderbilt University
• Bachelor of Science in Physics, Vanderbilt University

Glaws' research centers on applying machine learning and data-driven methods to renewable energy challenges. His core interests include:

• Machine Learning and Deep Learning
• Surrogate Modeling
• Uncertainty Quantification and Sensitivity Analysis
• Dimension Reduction
• Multifidelity Methods
• Exploratory Data/Model Analysis

He has extensive cross-domain collaborations in wind/solar energy, climate science, building energy analysis, bioenergy, and battery technology. Analysis of his 45 publications (2020-2025) reveals a pronounced shift toward generative AI applications in energy systems since 2022, with 2025 outputs emphasizing aerodynamic design, photovoltaic reliability, and offshore wind platform modeling using advanced neural architectures.

No scientific awards are documented in available sources.

His advisory activities and grant funding details are not specified in the provided materials, though his 28 Mendeley readers and active conference participation indicate significant academic engagement. Glaws operates within NREL's Computational Science Center, contributing to interdisciplinary teams tackling energy challenges through computational mathematics and AI-driven approaches.