Andrew Glaws is a researcher in applied mathematics in the Computational Science Center at NREL. He joined the lab as a postdoc in January 2019 to work on physics-informed deep learning for energy systems. His research focuses on enhancing scientific research into renewable energy and energy efficient problems using machine learning, artificial intelligence, and other data-driven methods. He has collaborated with domain scientists in a variety of energy-related fields, including wind and solar energy, climate science, buildings energy analysis, bioenergy, and battery technology. Prior to joining NREL, Andrew completed his Ph.D. in computer science at the University of Colorado Boulder, researching the use of parameter reduction methods for computational experiments.

Machine learning and deep learning

Surrogate modeling

Uncertainty quantification and sensitivity analysis

Dimension reduction

Multifidelity methods

Exploratory data/model analysis

Researcher – Applied Mathematics, NREL (2021–Present) 

Postdoctoral Researcher, NREL (2019–2021) 

Graduate Research Assistant, University of Colorado Boulder (2017–2018)