聚变工程资源库
检索带来源、版本、许可和访问条件的技术文档、科学软件与工程资源。
聚变机器学习研究需求报告
U.S. Department of EnergyDOE聚变能源科学与先进科学计算研究联合发布的报告，系统梳理AI加速科学发现、聚变运行与数据基础的优先方向。
DOE聚变科学机器学习项目清单
U.S. Department of Energy涵盖EFIT-AI平衡重建、聚变材料机器学习、射频建模加速和实时等离子体预测等研究项目。
FAIR-MAST聚变实验数据管理系统
UK Atomic Energy AuthorityUKAEA公开的聚变实验数据管理研究，面向可发现、可访问、可互操作、可复用的数据及AI/ML应用基础。
IAEA AI for Fusion研究协作资料
International Atomic Energy AgencyIAEA AI for Fusion协作研究项目会议与公开资料入口，汇集机器学习、聚变数据和跨机构研究议题。
聚变等离子体破裂深度学习预测
U.S. Department of EnergyDOE公开研究说明，介绍利用DIII-D与JET数据开展跨装置托卡马克破裂预测的方法与应用背景。
MHD, disruptions and control physics: Chapter 4 of the special issue: on the path to tokamak burning plasma operation
Nuclear FusionAbstract In this chapter, we review the progress in MHD stability, disruptions and control in magnetic fusion research that has occurred over the past (more than) one and a half decades since the publication by Hender et al in 2007 on the same topic as part…
Neural network-based classification and regression of magnetohydrodynamic modes in tokamaks
Nuclear FusionAbstract We present a machine learning-based magnetohydrodynamic (MHD) classifier and regressor that utilizes real or complex-valued 3D magnetic sensor array data to determine neoclassical tearing mode (NTM) onset times in tokamaks with millisecond accuracy…
Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
arXiv (Cornell University)Inertial fusion energy promises nearly unlimited, clean power if it can be achieved. However, the design and engineering of fusion systems requires controlling and manipulating matter at extreme energies and timescales; the shock physics and radiation trans…
Surrogate models for particle and power exhaust in divertor tokamak simulations
JuSER (Forschungszentrum Jülich)The heat exhaust constitutes one of the most critical operational limits in tokamak fusion reactors. Unmitigated, the expected heat fluxes in future reactors will exceed what is sustainable for known materials. The scrape-off layer connects the plasma and r…
A machine learning case study in nuclear fusion: Assessment of the absolute deuterium-tritium fusion power of ITER with gamma-ray spectroscopy
Energy and AINuclear fusion holds great potential as a carbon-neutral means of electricity production. However, technical aspects of its implementation remain challenging. The real-time measurement of the fusion power released during Deuterium-Tritium (DT) fusion is one…
Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
arXiv (Cornell University)Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often…
Regulation compliant AI for fusion: explainable image-based feedback control of divertor detachment in DIII-D tokamak
Nuclear FusionAbstract While artificial intelligence (AI) has been promising for fusion control, its inherent black-box nature will make compliant implementation in regulatory environments a challenge. This study implements and validates a real-time AI-enabled linear and…
Combining physics-based and data-driven models for quantitatively accurate plasma profile prediction that extrapolates well; with application to DIII-D, AUG, and ITER tokamaks
Nuclear FusionAbstract For design, scenario planning, and control, ITER and all other envisioned tokamaks rely on a variety of statistical and physics-based models to extrapolate to unseen regimes; most notably from low plasma current to high. A ‘meta-learning’ methodolo…
Preliminary machine learning-based calibration strategy for the ITER Tokamak Systems Monitor
Fusion Engineering and DesignA machine learning-based calibration strategy for the ITER Tokamak Systems Monitor (TSM) is presented, focusing on the optimization of dynamic finite-element method (FEM) models essential for monitoring and assessing the structural health of the tokamak. Tr…
Using deep learning for the detection of UFOs within the JET tokamak
Physics of PlasmasComponents on the inner wall of fusion reactors are exposed to extremely high heat loads, and significant care must be taken to minimize the damage to these components during operation. UFOs, also known as transient impurity events, are small particles of d…
Active ramp-down control and trajectory design for tokamaks with neural differential equations and reinforcement learning
Communications PhysicsThe tokamak offers a promising path to fusion energy, but disruptions pose a major economic risk, motivating solutions to manage their consequence. This work develops a reinforcement learning approach to this problem by training a policy to ramp-down the pl…
Machine learning based energy confinement time extrapolation via multi-tokamak database
Fusion Engineering and DesignPredicting energy confinement time in future fusion devices like ITER is a significant challenge for traditional methods. This study introduces an advanced machine learning framework to address this. Our approach utilizes deep ensembles for robust uncertain…
Macroscopic trends of neoclassical tearing stability in high-field H-mode tokamak pilot plants
Nuclear FusionAbstract The neoclassical tearing mode (NTM) stability metric—minimum marginally stable island width w m ∗ —was compared across 14651 inductive high-field tokamak pilot plant equilibria. Larger devices with reduced elongation and/or increased minor radius d…
High-throughput full-f gyrokinetics of the tokamak boundary
arXiv (Cornell University)Full-f global gyrokinetic simulations of the plasma boundary have until now required heroic computational efforts and case-by-case expert intervention, precluding systematic parameter scans. Here we demonstrate a paradigm shift: hundreds of independent, con…