most citedImproving Neural Additive Models with Bayesian Principles

2 citations · 2 across the 3 of their papers we have counts for

collaborators
Showing physics.plasm-phShow all

2 papers · 1 filter

physics.plasm-ph2026

IGNITE Tokamak World Model Architecture

Peter Steiner, Azarakhsh Jalalvand, Nathaniel Chen +4

We introduce IGNITE, a generative world foundation model for fusion plasma behavior simulation trained in a self-supervised manner from over a decade of unlabeled experimental data…

physics.plasm-ph2026

Towards Large-Scale Heterogeneous Data Organization for Scientific Foundation Models: A Nuclear Fusion Case Study

Nathaniel Chen, Kouroche Bouchiat, Peter Steiner +3

Training effective foundation models requires massive and organized datasets, yet scientific domains such as nuclear fusion present unique challenges due to largely heterogeneous a…