Speaker
Description
IGNITE is a multi-modal world model for full discharge simulation, trained on a large-scale dataset of DIII-D experimental data. This world model has been developed as part of the DOE Fusion Science & Technology Roadmap [1]. Inspired by the success of scientific world models for weather and climate forecasting [2,3,4], IGNITE is an end-to-end AI model that ingests heterogeneous diagnostic signals, videos, and actuators and autoregressively forecasts the plasma state of a DIII-D experiment, relying on a pre-defined actuator trajectory and on an initially measured plasma state. The model was trained on a large dataset of DIII-D discharges, covering more than ten years of experimentation (DIII-D shot numbers 160000 - present). For dataset collection and model training, the Fusion AI Toolkit and Hub (FAITH) [5,6] was used, and the source-code is publicly available in the git repository [5].
IGNITE can deal with signals at different time scales, from slowly evolving signals such as electron temperature measured by Thomson Scattering to high resolution signals such as interferometer and electron cyclotron emission radiometer. In addition, IGNITE can deal with camera data like tangential TV. The autoregressive predictions are window based, meaning that IGNITE processes windows of 50ms of data. Different sampling frequencies are taken into account by encoding each modality independently. Figure 1 shows an example of autoregressively predicting different modalities with IGNITE.
The authors gratefully acknowledge the collaboration of the DIII-D Team supported by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, using the National Fusion Facility, a DOE Office of Science user facility, under Award No. DE-FC02-04ER54698 and DE-SC0024527. Additional support was provided by the Princeton Laboratory for Artificial Intelligence under Award No. 2025-97. In addition, this work was supported by the US Department of Energy Contract DE-AC02-09CH11466.
This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Advanced Scientific Computing Research programs in the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
