AI-机器学习赋能的托卡马克数字孪生
AI-Machine Learning-Enabled Tokamak Digital Twin
AI总结：
本文提出普林斯顿大学与NVIDIA合作，利用AI深度学习近实时模拟器和Omniverse平台构建托卡马克数字孪生，以加速聚变装置建模与控制。
摘要：
为响应美国能源部2022年4月宣布的“大胆十年愿景”——即在2035年前交付聚变试验电厂，需要开发相关软件工具，将现实世界的工程与供应链数据同经机器学习加速的先进科学模型相集成。本文介绍了一项相关的研究与开发工作，在构建逼真的托卡马克数字孪生方面取得了令人鼓舞的早期进展。该工作受益于普林斯顿大学AI深度学习创新的近实时模拟器所推动的加速进展，以及NVIDIA Omniverse提供的技术能力。Omniverse是一个开放式计算平台，用于构建和运行与领先科学计算可视化软件相连接的应用。以GA/DIII-D托卡马克的CAD文件（包括平衡演化）作为Omniverse上的示范性托卡马克应用，普林斯顿大学与NVIDIA的合作已将现代AI/HPC赋能的近实时动力学集成进来，以连接并加速最先进的合成HPC模拟器，从而对聚变装置和控制系统进行建模。其总体目标是交付一个先进磁约束聚变能托卡马克的交互式科学数字孪生，该数字孪生能够支持基于Omniverse构建的近实时模拟工作流，最终为开辟清洁能源发电新能力、创造更美好未来打开大门。
英文摘要：
In addressing the Department of Energy's April, 2022 announcement of a Bold Decadal Vision for delivering a Fusion Pilot Plant by 2035, associated software tools need to be developed for the integration of real world engineering and supply chain data with advanced science models that are accelerated with Machine Learning. An associated research and development effort has been introduced here with promising early progress on the delivery of a realistic Digital Twin Tokamak that has benefited from accelerated advances by the Princeton University AI Deep Learning innovative near-real-time simulators accompanied by technological capabilities from the NVIDIA Omniverse, an open computing platform for building and operating applications that connect with leading scientific computing visualization software. Working with the CAD files for the GA/DIII-D tokamak including equilibrium evolution as an exemplar tokamak application using Omniverse, the Princeton-NVIDIA collaboration has integrated modern AI/HPC-enabled near-real-time kinetic dynamics to connect and accelerate state-of-the-art, synthetic, HPC simulators to model fusion devices and control systems. The overarching goal is to deliver an interactive scientific digital twin of an advanced MFE tokamak that enables near-real-time simulation workflows built with Omniverse to eventually help open doors to new capabilities for generating clean power for a better future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
