Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science
E. Wes Bethel, Vianna Cramer, Alexander del Rio, Lothar Narins, Chris Pestano, Satvik Verma, Erick Arias, Nicola Bertelli, Talita Perciano, Syun'ichi Shiraiwa, Álvaro Sánchez Villar, Greg Wallace
arXiv:2409.06122v1
Originality Synthesis-oriented
AI Analysis
This addresses the need for efficient simulation models in fusion energy research, but it appears incremental as it builds on existing methods.
The study tackled the problem of modeling radio frequency heating in fusion energy science by using Generative AI to build AI-based surrogates and regressors, resulting in a comparison with manually developed models.
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.