In the realm of fusion energy research, a team of scientists from Princeton University, including Misha Padidar, Teresa Huang, Andrew Giuliani, and Marina Spivak, has been exploring innovative ways to streamline the design process for stellarators. These advanced fusion devices, which aim to harness the power of nuclear fusion for clean energy, have traditionally been a challenge to design due to their complex magnetic field configurations.
The researchers have turned to machine learning, specifically generative AI, to accelerate this process. They have framed the design of stellarators as an inverse problem, challenging the machine learning community to rapidly generate high-quality designs with desirable characteristics. In a case study, they trained a conditional diffusion model using data from the QUASR database to create quasisymmetric stellarator designs with specific aspects such as aspect ratio and mean rotational transform.
The diffusion model was then used to design stellarators with characteristics not seen during training. The results were promising, with many of the generated designs exhibiting less than 5% deviation from quasisymmetry and the target characteristics. This modest deviation indicates potential for further improvement, aiming for a sub 1% target.
The practical applications for the energy sector are significant. By speeding up the design process, this approach could accelerate the development of fusion power plants, bringing us closer to a future powered by clean, abundant fusion energy. The researchers also highlight multiple promising avenues for generative modeling to advance stellarator design, suggesting that this is just the beginning of a transformative approach to fusion research.
This research was published in the journal Nature Communications.
This article is based on research available at arXiv.