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arXiv 2310.09024physics.plasm-ph
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托卡马克湍流代理模型的高效训练集:基于主动深度集成方法

Efficient training sets for surrogate models of tokamak turbulence with Active Deep Ensembles

L. Zanisi, A. Ho, T. Madula, J. Barr, J. Citrin, S. Pamela, J. Buchanan, F. Casson, V. Gopakumar, JET contributors

AI总结:

提出基于物理信息的主动深度集成(ADEPT)两阶段主动学习策略,高效生成托卡马克湍流代理模型训练集,实现20倍数据缩减,并以优于10%精度恢复QuaLiKiz性能。

摘要:

基于模型的等离子体情景开发是未来聚变电厂设计和运行的核心。在集成模型中包含湍流输运对于成功实现ITER运行和DEMO级装置设计的路线图至关重要。鉴于集成模型的高度迭代特性,基于机器学习的快速湍流输运代理模型对于满足更快模拟的迫切需求至关重要,这为脉冲设计、优化和飞行模拟器应用开辟了道路。一个显著的瓶颈是生成足够大的训练数据集以覆盖参数空间中的大范围区域,而对于更高保真度的代码而言,获取这些数据可能代价高昂。\n\n在这项工作中,我们提出了ADEPT(主动深度集成用于等离子体湍流),这是一种基于物理信息的、两阶段主动学习策略,以缓解这一挑战。主动学习通过采集函数查询给定模型,该函数识别需要额外数据以改进代理模型的区域。我们利用文献中可用的QuaLiKiz准线性输运模型数据进行了基准研究。我们定量证明了所提出工作流的物理信息特性减少了对参数空间稳定区域进行模拟的需求,从而显著提高了数据效率。我们展示了在达到与随机采样相同性能时,所需训练数据集大小最多可减少20倍。随后,我们在ITG主导的JET情景的多通道集成建模中验证了代理模型,并证明它们以优于10%的精度恢复了QuaLiKiz的性能。这与先前工作的性能相匹配,但训练数据点数量减少了两个数量级。

英文摘要:

Model-based plasma scenario development lies at the heart of the design and operation of future fusion powerplants. Including turbulent transport in integrated models is essential for delivering a successful roadmap towards operation of ITER and the design of DEMO-class devices. Given the highly iterative nature of integrated models, fast machine-learning-based surrogates of turbulent transport are fundamental to fulfil the pressing need for faster simulations opening up pulse design, optimization, and flight simulator applications. A significant bottleneck is the generation of suitably large training datasets covering a large volume in parameter space, which can be prohibitively expensive to obtain for higher fidelity codes. In this work, we propose ADEPT (Active Deep Ensembles for Plasma Turbulence), a physics-informed, two-stage Active Learning strategy to ease this challenge. Active Learning queries a given model by means of an acquisition function that identifies regions where additional data would improve the surrogate model. We provide a benchmark study using available data from the literature for the QuaLiKiz quasilinear transport model. We demonstrate quantitatively that the physics-informed nature of the proposed workflow reduces the need to perform simulations in stable regions of the parameter space, resulting in significantly improved data efficiency. We show an up to a factor of 20 reduction in training dataset size needed to achieve the same performance as random sampling. We then validate the surrogates on multichannel integrated modelling of ITG-dominated JET scenarios and demonstrate that they recover the performance of QuaLiKiz to better than 10\%. This matches the performance obtained in previous work, but with two orders of magnitude fewer training data points.

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。

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