PHY359S IFS Seminar

Stellarators are a promising magnetic confinement approach for achieving controlled nuclear fusion. However, stellarator design remains challenging, as evaluating configuration performance requires computationally expensive, high-fidelity simulations. Machine learning (ML) has the potential to mitigate this problem by providing surrogates and generative procedures for rapidly designing stellarator configurations. In this talk, we will discuss recent advancements in the development of large-scale datasets for stellarator design and show how generative models can be used to accelerate the design process.

 

Research Fellow, Center for Computational Mathematics, Flatiron Institute

 

Misha received a Bachelor of Science from the University of California, Santa Barbara, and a Ph.D. and M.Sc. in applied mathematics from Cornell University. After working as a machine learning engineer at Uber, Misha joined the Flatiron Institute as a research fellow in the Center for Computational Mathematics. His research primarily focuses on numerical optimization and AI for science, with the goal of advancing fusion energy and the stellarator concept.