Deep learning surrogates for CFD flow-field prediction often rely on large, complex models, which can be slow and fragile when data are noisy or incomplete. We introduce FlowForge, a staged local rollout engine that predicts future flow fields by compiling a locality-preserving update schedule and executing it with a shared lightweight local predictor. Rather than producing the next frame in a single global pass, FlowForge rewrites spatial sites stage by stage so that each update conditions only on bounded local context exposed by earlier stages. This compile-execute design aligns inference with short-range physical dependence, keeps latency predictable, and limits error amplification from global mixing. Across PDEBench, CFDBench, and BubbleML, FlowForge matches or improves upon strong baselines in pointwise accuracy, delivers consistently better robustness to noise and missing observations, and maintains stable multi-step rollout behavior while reducing per-step latency.

翻译：用于计算流体力学流场预测的深度学习替代模型通常依赖庞大而复杂的网络结构，这类模型在数据存在噪声或不完整时可能运行缓慢且鲁棒性不佳。本文提出FlowForge——一种分阶段局部滚动引擎，通过构建保持局部性的更新调度表，并借助共享的轻量级局部预测器执行该调度表来实现未来流场的预测。与传统单次全局推理生成下一帧的方式不同，FlowForge逐阶段重写空间位置，使每次更新仅依赖于先前阶段暴露的有限局部上下文。这种“编译-执行”设计使推理过程与短程物理依赖关系保持一致，不仅保证了延迟的可预测性，还能有效抑制全局混合导致的误差放大效应。在PDEBench、CFDBench和BubbleML数据集上的实验表明，FlowForge在逐点精度上与强基线方法持平或更优，对噪声及缺失观测始终展现出更优异的鲁棒性，同时保持稳定的多步滚动行为，并降低了单步推理延迟。