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.

翻译：高分辨率、时空演化场（由大规模高维偏微分方程控制）的持久存储需求已达到PB到EB级别。模拟纳维-斯托克斯方程、磁流体动力学、等离子体物理或双黑洞合并的瞬态仿真所生成的数据量对现代高性能计算基础设施构成了严峻挑战。为解决这一瓶颈，我们提出ANTIC（自适应神经时间原位压缩器），这是一种端到端的原位压缩流水线。ANTIC包含一个面向高维物理的自适应时间选择器，可在仿真运行时识别并过滤包含信息的快照，同时结合基于连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，ANTIC实现了时空分量的联合压缩，有效避免了将完整时间演化轨迹显式存储在磁盘上的需求。实验结果表明，存储量减少数个数量级与物理精度之间存在明确关联。