发表机构
Oak Ridge National Laboratory; Grand Valley State University; University of Texas at Austin（橡树岭国家实验室; 大峡谷州立大学; 德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出PreVAE-Turb，利用预训练VAE与ConvLSTM构建湍流代理模型，通过谱损失微调并验证于陀动动力学湍流，实现高效加速模拟。
AI 中文摘要
机器学习代理模型为加速等离子体湍流模拟提供了一条有前景的路径。我们提出了PreVAE-Turb，一个利用来自Stable Diffusion图像生成模型的预训练变分自编码器（VAEs）对湍流场进行高效空间压缩的代理建模框架。预训练的VAE通过一个物理信息损失函数在湍流数据上进行微调，该损失函数包含一个在傅里叶空间中操作的谱损失，以强制跨尺度的谱精度。该VAE与卷积长短期记忆（ConvLSTM）网络相结合，在潜在空间中学习时间动态，并采用流形一致性误差度量来监控自回归滚动过程中的编码-解码一致性。我们在二维Hasegawa-Wakatani漂移波湍流上验证了该框架，并将其扩展到来自GENE代码的陀动动力学湍流，其中四通道适配同时预测静电势、密度以及平行/垂直温度波动，无需重新设计架构。一旦训练完成，推理在单个GPU上数秒内生成数千个时间步，与直接数值模拟相比提供了显著的计算加速。预训练方法提供了一种可迁移的方法论，广泛适用于各种湍流模拟代码。
英文摘要
Machine learning surrogate models offer a promising path toward accelerating plasma turbulence simulations. We present PreVAE-Turb, a surrogate modeling framework that leverages pre-trained variational autoencoders (VAEs) from the Stable Diffusion image generation model for efficient spatial compression of turbulence fields. The pre-trained VAE is fine-tuned on turbulence data using a physics-informed loss function that includes a spectral loss operating in Fourier space to enforce spectral accuracy across scales. The VAE is combined with convolutional long short-term memory (ConvLSTM) networks to learn temporal dynamics in latent space, with a manifold consistency error metric that monitors encode--decode consistency during autoregressive rollouts. We validate the framework on two-dimensional Hasegawa-Wakatani drift-wave turbulence and extend it to gyrokinetic turbulence from the GENE code, where a four-channel adaptation simultaneously predicts electrostatic potential, density, and parallel/perpendicular temperature fluctuations without requiring architecture redesign. Once trained, inference generates thousands of time steps in seconds on a single GPU, providing substantial computational acceleration compared to direct numerical simulation. The pre-trained approach offers a transferable methodology broadly applicable to various turbulence simulation codes.
