发表机构
Princeton University; Princeton Plasma Physics Laboratory（普林斯顿大学; 普林斯顿等离子体物理实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
IGNITE是一个基于DIII-D十年数据训练的自监督生成式世界模型，通过时空分词器和自回归动力学模型，从执行器轨迹预测托卡马克放电，推动AI驱动的聚变实验规划。
AI 中文摘要
我们引入了IGNITE，这是一个生成式世界基础模型，用于聚变等离子体行为模拟，该模型通过自监督方式，利用DIII-D国家聚变设施超过十年的未标记实验数据进行训练。IGNITE的核心是一个动力学模型，能够从给定的执行器轨迹集合模拟DIII-D放电过程。这些轨迹可以由用户提供，或根据文本提示或期望的实验结果即时生成。模型架构包含多个时空分词器，用于嵌入不同的输入模态，包括时间序列（如时空测量数据）、图像序列和高分辨率光谱图，每种模态均以截然不同的时间尺度采集。骨干网络由一个自回归动力学模型组成，该模型能够根据初始潜在等离子体状态和执行器轨迹，在理论无限时间范围内预测完整的DIII-D放电过程。IGNITE为高效的人工智能驱动实验规划及核聚变世界建模铺平了道路。
英文摘要
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.
