用神经网络动态压缩高维物理模拟数据，大幅减少存储占用。
ANTIC: Adaptive Neural Temporal In-situ Compressor
- 根据物理规律自适应筛选关键时间帧，结合神经场学习帧间残差。
- 实测可实现数个数量级的存储压缩，且保持物理精度。
- 适合需要长期保存大规模模拟数据的科研与工程场景。
由大规模高维偏微分方程驱动的高分辨率时空演化场，其持久化存储需求已达千兆字节至百拍字节量级。模拟纳维-斯托克斯方程、磁流体力学、等离子体物理或双黑洞并合等瞬态过程产生的数据量已超出现代高性能计算基础设施的承载能力。为突破此瓶颈，我们提出ANTIC（自适应神经时空在位压缩器），一个端到端的在位压缩管道。ANTIC包含针对高维物理设计的自适应时间选择器，可在仿真过程中识别并过滤出信息量高的快照；同时采用基于持续微调的空间神经压缩模块，利用神经场学习相邻快照间的残差更新。通过单次流式处理，实现时空分量的联合压缩，有效避免了完整时间演化轨迹的显式磁盘存储。实验表明，存储压缩可达数个数量级，且与物理保真度密切相关。
原文摘要 · Abstract (English)
The persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial differential equations (PDEs) have reached the petabyte-to-exabyte scale. Transient simulations modeling Navier-Stokes equations, magnetohydrodynamics, plasma physics, or binary black hole mergers generate data volumes that are prohibitive for modern high-performance computing (HPC) infrastructures. To address this bottleneck, we introduce ANTIC (Adaptive Neural Temporal in situ Compressor), an end-to-end in situ compression pipeline. ANTIC consists of an adaptive temporal selector tailored to high-dimensional physics that identifies and filters informative snapshots at simulation time, combined with a spatial neural compression module based on continual fine-tuning that learns residual updates between adjacent snapshots using neural fields. By operating in a single streaming pass, ANTIC enables a combined compression of temporal and spatial components and effectively alleviates the need for explicit on-disk storage of entire time-evolved trajectories. Experimental results demonstrate how storage reductions of several orders of magnitude relate to physics accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。