Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

TL;DR

This paper explores how Generative AI can be used to create surrogate models for simulating radio frequency heating in fusion energy, aiming to improve efficiency and accuracy over traditional methods.

Contribution

It introduces a novel application of GenAI for developing AI surrogates in fusion energy modeling, demonstrating its effectiveness compared to manual approaches.

Findings

GenAI-based surrogates outperform manual models in accuracy.

The approach reduces development time for simulation models.

GenAI enhances the optimization process in fusion energy simulations.

Abstract

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.