Ignite model simulates fusion plasma behavior from experimental data
IGNITE Tokamak World Model Architecture
Artificial IntelligenceMachine Learning
Summary
Simulating how plasma behaves during fusion experiments is very complex and important for developing fusion energy. The authors created IGNITE, a model trained on over ten years of real fusion reactor data to predict plasma behavior based on control inputs. It takes multiple types of experimental data and can simulate entire fusion experiments from start to finish. This approach could help plan fusion experiments more efficiently using AI.
What this means in practice
- •For fusion experiment planners: Simulate plasma behavior to design and optimize future fusion experiments using actuator controls and predicted outcomes.
- •For energy systems engineers: Integrate plasma simulation results into wider fusion power system designs and testing setups to improve overall system efficiency.
Authors
Peter Steiner, Azarakhsh Jalalvand, Nathaniel Chen, Kouroche Bouchiat, Ricardo Shousha, SangKyeun Kim, Egemen Kolemen
Abstract
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 at the DIII-D National Fusion Facility. The core of IGNITE is a dynamics model that can simulate DIII-D discharges from a given set of actuator trajectories. These trajectories can be supplied or generated on-the-fly from a textual prompt or from desired experimental outcomes. The model architecture consists of several spatio-temporal tokenizers that embed the different input modalities, including time-series like spatio-temporal measurement data, image sequences, and high-resolution spectrograms, each of which collected at vastly different time scales. The backbone is composed of an auto-regressive dynamics model that has the capacity to predict entire DIII-D discharges given initial latent plasma states and actuator trajectories over a theoretical infinite horizon. IGNITE paves the way towards efficient AI-driven experimental planning and world modeling for nuclear fusion.