Using Deep Learning to Design High Aspect Ratio Fusion Devices
This work addresses the design bottleneck for fusion energy researchers by providing a more efficient method, though it appears incremental as it builds on existing high aspect ratio models.
The paper tackled the computationally expensive design of fusion devices, particularly stellarators, by using a machine learning model based on mixture density networks to solve the inverse design problem, generating optimized configurations with favorable confinement properties reliably.
The design of fusion devices is typically based on computationally expensive simulations. This can be alleviated using high aspect ratio models that employ a reduced number of free parameters, especially in the case of stellarator optimization where non-axisymmetric magnetic fields with a large parameter space are optimized to satisfy certain performance criteria. However, optimization is still required to find configurations with properties such as low elongation, high rotational transform, finite plasma beta, and good fast particle confinement. In this work, we train a machine learning model to construct configurations with favorable confinement properties by finding a solution to the inverse design problem, that is, obtaining a set of model input parameters for given desired properties. Since the solution of the inverse problem is non-unique, a probabilistic approach, based on mixture density networks, is used. It is shown that optimized configurations can be generated reliably using this method.