A proof-of-concept for automated AI-driven stellarator coil optimization with in-the-loop finite-element calculations
This work addresses a critical bottleneck in stellarator design for fusion energy research, offering incremental improvements through automation and new optimization capabilities.
The paper tackles the challenge of designing feasible coils for stellarator fusion devices by developing an automated end-to-end runner for coil optimization, which speeds up the workflow and includes novel in-the-loop finite-element calculations for Von Mises stresses.
Finding feasible coils for stellarator fusion devices is a critical challenge of realizing this concept for future power plants. Years of research work can be put into the design of even a single reactor-scale stellarator design. To rapidly speed up and automate the workflow of designing stellarator coils, we have designed an end-to-end ``runner'' for performing stellarator coil optimization. The entirety of pre and post-processing steps have been automated; the user specifies only a few basic input parameters, and final coil solutions are updated on an open-source leaderboard. Two policies are available for performing non-stop automated coil optimizations through a genetic algorithm or a context-aware LLM. Lastly, we construct a novel in-the-loop optimization of Von Mises stresses in the coils, opening up important future capabilities for in-the-loop finite-element calculations.