FlowForge通过分阶段局部更新，提升流场预测的精度与鲁棒性。
FlowForge: A Staged Local Rollout Engine for Flow-Field Prediction
- 分阶段局部更新，每步仅依赖有限邻域上下文。
- 在多个基准上精度超越或媲美强基线，噪声和缺失数据下更稳定。
- 适合需要低延迟、高可靠性的实时流场预测场景。
深度学习替代模型常用于计算流体力学（CFD）流场预测，但依赖大型复杂模型，数据噪声或缺失时易变慢且脆弱。我们提出FlowForge，一种分阶段局部滚动执行引擎，通过编译保留局部性的更新调度，并用共享轻量级局部预测器执行。不同于单次全局推断下一帧，FlowForge逐阶段重写空间位置，使每次更新仅依赖前序阶段暴露的有限局部上下文。该编译-执行设计使推理契合短程物理依赖，保持延迟可预测，抑制全局混合导致的误差传播。在PDEBench、CFDBench和BubbleML上，FlowForge在点级精度上匹配或超越强基线，对噪声和缺失观测表现出更强鲁棒性，并在多步滚动中维持稳定行为，同时降低每步延迟。
原文摘要 · Abstract (English)
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.
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