Research Output per Year
Research Output per Year
Overview
Personal Profile
Dr. Ryan King is a senior scientist in the Complex Systems Simulation & Optimization Group within the Computational Science Center. His research focuses on optimization, machine learning, and uncertainty quantification (UQ) applied to complex energy systems and turbulent flows. Ryan leads projects on physics-informed deep learning, wind farm surrogate modeling and optimization, and multi-fidelity UQ. During his Ph.D., Ryan developed adjoint optimization techniques to improve wind plant design and created a new data-driven machine learning closure for turbulence modeling in large eddy simulations. Prior to graduate school, Ryan worked as an engineer at RES Americas where he was involved in the design and construction of over 750 MW of operational wind energy.
Research Interests
Turbulent flows
Deep learning
Uncertainty quantification
Stochastic optimization
Adjoint methods
Professional Experience
Energy Resource Engineer & Turbine Engineer, Renewable Energy Systems Americas Inc. (2009–2012)
Education/Academic Qualification
Bachelor, Mechanical Engineering, Massachusetts Institute of Technology
PhD, Mechanical Engineering, University of Colorado
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Collaborations and Top Research Areas From the Past 5 Years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Artificial Intelligence-Aided Wind Plant Optimization for Nationwide Evaluation of Land Use and Economic Benefits of Wake Steering
Harrison-Atlas, D., Glaws, A., King, R. & Lantz, E., 2024, In: Nature Energy. 9, p. 735-749 15 p.Research output: Contribution to journal › Article › peer-review
7 Scopus Citations -
High-Resolution Meteorology with Climate Change Impacts from Global Climate Model Data Using Generative Machine Learning
Buster, G., Benton, B., Glaws, A. & King, R., 2024, In: Nature Energy. 9, 7, p. 894-906 13 p.Research output: Contribution to journal › Article › peer-review
10 Scopus Citations -
Multi-Fidelity Modeling and Control for Building Temperature Control
Wald, D., Johnson, K., Doronina, O., Chintala, R., King, R., Vaidhynathan, D. & Sanyal, J., 2024, 28 p.Research output: NREL › Presentation
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PINN Surrogate of Li-Ion Battery Models for Parameter Inference, Part I: Implementation and Multi-Fidelity Hierarchies for the Single-Particle Model: Article No. 113103
Hassanaly, M., Weddle, P., King, R., De, S., Doostan, A., Randall, C., Dufek, E., Colclasure, A. & Smith, K., 2024, In: Journal of Energy Storage. 98, Part B, 13 p.Research output: Contribution to journal › Article › peer-review
4 Scopus Citations -
PINN Surrogate of Li-Ion Battery Models for Parameter Inference, Part II: Regularization and Application of the Pseudo-2D Model: Article No. 113104
Hassanaly, M., Weddle, P., King, R., De, S., Doostan, A., Randall, C., Dufek, E., Colclasure, A. & Smith, K., 2024, In: Journal of Energy Storage. 98, Part B, 17 p.Research output: Contribution to journal › Article › peer-review
5 Scopus Citations
Awards and Honors
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NREL Outstanding Mentor Award
Salvachua Rodriguez, D. (Recipient), Woods, J. (Recipient), Neale, N. (Recipient) & King, R. (Recipient), 2019
Prize: Honorary award
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NREL Outstanding Mentor Award
Hodge, B.-M. (Recipient), Hamilton, N. (Recipient) & King, R. (Recipient), 2018
Prize: Honorary award