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arXiv 2405.13232physics.plasm-phphysics.comp-ph
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等离子体湍流的生成式机器学习代理模型

A generative machine learning surrogate model of plasma turbulence

B. Clavier, D. Zarzoso, D. del-Castillo-Negrete, E. Frenod

AI总结:

首次提出基于卷积变分自编码器与循环神经网络的 GAIT 生成式代理模型,将等离子体湍流模拟速度提升 400 倍,并在多种谱、拓扑及输运特征上与 HW 模型高度一致。

摘要:

首次采用生成式人工智能方法构建了等离子体湍流的代理模型,从而实现了长时间输运模拟。所提出的 GAIT(Generative Artificial Intelligence Turbulence)模型基于卷积变分自编码器与循环神经网络和解码器的耦合,其中编码器将预计算的湍流数据编码至降维潜空间,后者生成新湍流状态的速度比直接数值积分快 400 倍。该模型被应用于 Hasegawa-Wakatani (HW) 等离子体湍流模型,该模型与地球物理流体力学中使用的准地转模型密切相关。在时空傅里叶和本征正交分解谱,以及由 Okubo-Weiss 分解表征的流动拓扑方面,发现 GAIT 与 HW 模型之间具有极好的一致性。GAIT 模型还重现了拉格朗日输运,包括粒子位移的概率分布函数和有效湍流扩散率。

英文摘要:

Generative artificial intelligence methods are employed for the first time to construct a surrogate model for plasma turbulence that enables long time transport simulations. The proposed GAIT (Generative Artificial Intelligence Turbulence) model is based on the coupling of a convolutional variational auto-encoder, that encodes precomputed turbulence data into a reduced latent space, and a recurrent neural network and decoder that generates new turbulence states 400 times faster than the direct numerical integration. The model is applied to the Hasegawa-Wakatani (HW) plasma turbulence model, that is closely related to the quasigeostrophic model used in geophysical fluid dynamics. Very good agreement is found between the GAIT and the HW models in the spatio-temporal Fourier and Proper Orthogonal Decomposition spectra, and the flow topology characterized by the Okubo-Weiss decomposition. The GAIT model also reproduces Lagrangian transport including the probability distribution function of particle displacements and the effective turbulent diffusivity.

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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