arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.04758physics.plasm-ph
收藏

物理引导的神经压缩用于高维等离子体数据

Physics-Informed Neural Compression of High-Dimensional Plasma Data

JKU Linz(林茨约翰·开普勒大学);United Kingdom Atomic Energy Authority, Culham campus(英国原子能管理局卡尔汉姆校区);EMMI AI GmbH, Linz(林茨EMMI人工智能有限公司)

Gianluca Galletti, Gerald Gutenbrunner, Sandeep S. Cranganore, William Hornsby, Lorenzo Zanisi, Naomi Carey, Stanislas Pamela, Johannes Brandstetter, Fabian Paischer

AI总结:

本文提出物理引导的神经压缩方法,用于高效压缩高维等离子体数据,实现超过70000倍的压缩率,以应对存储瓶颈并支持后处理分析。

摘要:

高保真科学模拟现在产生前所未有的大量数据,造成存储和分析瓶颈。单个模拟可以生成巨大的数据量,往往迫使研究人员丢弃有价值的信息。一个典型的例子是通过回旋动力学方程描述的等离子体湍流:非线性、多尺度且在相空间中为5维。它构成了现代科学最计算密集的前沿之一,运行时间可达数周,产生数十TB的数据输出。日益增长的存储需求凸显了压缩的重要性。然而,重建的快照不一定保持关键的物理量。我们提出了一种时空评估流程,考虑结构现象和多尺度瞬态波动以评估物理保真度的程度。事实上,我们发现各种压缩技术缺乏对空间模式结构和时间湍流特征的保留。因此,我们探索了物理引导的神经压缩(PINC),它结合了针对回旋动力学定制的物理引导损失,实现了超过70000倍的压缩率。在PINC之上进行熵编码进一步将其提升至120000倍。这一方向提供了一种可行且可扩展的解决方案,以应对回旋动力学的存储需求,使之前不可行的后处理分析成为可能。

英文摘要:

High-fidelity scientific simulations are now producing unprecedented amounts of data, creating a storage and analysis bottleneck. A single simulation can generate tremendous data volumes, often forcing researchers to discard valuable information. A prime example of this is plasma turbulence described by the gyrokinetic equations: nonlinear, multiscale, and 5D in phase space. It constitutes one of the most computationally demanding frontiers of modern science, with runs taking weeks and yielding tens of terabytes of data dumps. The increasing storage demands underscore the importance of compression. However, reconstructed snapshots do not necessarily preserve essential physical quantities. We present a spatiotemporal evaluation pipeline, accounting for structural phenomena and multi-scale transient fluctuations to assess the degree of physical fidelity. Indeed, we find that various compression techniques lack preservation of both spatial mode structure and temporal turbulence characteristics. Therefore, we explore Physics-Informed Neural Compression (PINC), which incorporates physics-informed losses tailored to gyrokinetics and enables extreme compressions ratios of over 70,000x. Entropy coding on top of PINC further pushes it to 120,000x. This direction provides a viable and scalable solution to the prohibitive storage demands of gyrokinetics, enabling post-hoc analyses that were previously infeasible.

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。

↑