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.

Research Interests

Machine learning and deep learning

Surrogate modeling

Uncertainty quantification and sensitivity analysis

Dimension reduction

Multifidelity methods

Exploratory data/model analysis

Education

Ph.D., Computer Science, University of Colorado Boulder

M.S., Mathematics, Virginia Polytechnic Institute and State University

B.A., Mathematics, Vanderbilt University

B.A., Physics, Vanderbilt University

Professional Experience

Researcher – Applied Mathematics, NREL (2021–Present) 

Postdoctoral Researcher, NREL (2019–2021) 

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

Featured Work

Adversarial Super-Resolution of Climatological Wind and Solar Data, Proceedings of the National Academy of Sciences of the United States of America (2020)

Deep Learning for in Situ Data Compression of Large Turbulent Flow Simulations, Physical Review Fluids (2020)

Unified Architecture for Data-Driven Metadata Tagging of Building Automation Systems, Automation in Construction (2020)

A Probabilistic Approach To Estimating Wind Farm Annual Energy Production With Bayesian Quadrature, American Institute of Aeronautics and Astronautics SciTech Forum (2020)