@misc{a1a6e0a316914c80b33d9512aed982c8,
title = "Enabling Innovation in Wind Turbine Design Using Artificial Intelligence",
abstract = "The Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements (INTEGRATE) project is developing a new inverse-design capability for wind turbine rotors using invertible neural networks. This artificial intelligence (AI)-based technology can capture complex nonlinear aerodynamic effects 100 times faster than alternative design approaches.",
keywords = "aerodynamics, airfoil, artificial intelligence, blade, design, inverse design, machine learning, neural networks, wind energy",
author = "Ganesh Vijayakumar and Andrew Glaws and Bumseok Lee and Jung, \{Yong Su\} and James Baeder and Olga Doronina and Zachary Grey and John Jasa and Koushik Marepally and Ryan King and Shreyas Ananthan",
year = "2022",
language = "American English",
series = "Presented at the ARPA-E Energy Innovation Summit, 23-25 May 2022, Denver, Colorado",
type = "Other",
}