Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science
<|reference_start|>Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science: This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.<|reference_end|>
@article{bethel2024case,
    title={Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors
  for Modeling Radio Frequency Heating in Fusion Energy Science},
    author={E. Wes Bethel, Vianna Cramer, Alexander del Rio, Lothar Narins, Chris
  Pestano, Satvik Verma, Erick Arias, Nicola Bertelli, Talita Perciano,
  Syun'ichi Shiraiwa, 'Alvaro S'anchez Villar, Greg Wallace, John C. Wright},
    journal={arXiv preprint arXiv:2409.06122},
    year={2024},
    number={LBNL-2001609},
    archivePrefix={arXiv},
    eprint={2409.06122},
    primaryClass={cs.AI cs.SE}
}