3 citations · 3 across the 2 of their papers we have counts for
3 papers
Generative modeling for fusion: a large-scale benchmark of stellarator fast-ion simulations
Misha Padidar, Teresa Huang, Mark Goldstein +2
Stellarators offer a promising magnetic confinement approach for achieving controlled nuclear fusion. However, stellarator design remains challenging, as evaluating configuration p…
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Yifan Chen, Mark Goldstein, Mengjian Hua +3
We propose a framework for probabilistic forecasting of dynamical systems based on generative modeling. Given observations of the system state over time, we formulate the forecasti…
SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
Nanye Ma, Mark Goldstein, Michael S. Albergo +3
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for…