Micro-kinetic modeling of temporal analysis of products data using kinetics-informed neural networks
Kinetics-informed neural networks improve fit quality for multi-pulse and noisy temporal analysis of products datasets.
Nai, Dingqi↗
Engineering topics
Boukouvala, Fani
Publications and source records attributed to Boukouvala, Fani.
Physics-informed machine learning with optimization-based guarantees: Applications to AC power flow
Not Available
97 MATHEMATICS AND COMPUTING↗
REDLY
SAND2025-11838O REDLY provides a framework for designing, training, and validating physics-informed neural networks for power system applications. It trains surrogates for Alternating Current Power Flow prediction and general economic dispatch problems, such as Alternating Current Optimal Power Flow and Direct Current Power Flow. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Blakely, Logan↗