Abstract: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.
| Comments: | Main paper: 13 pages, 6 figures, 2 tables. Appendix: 17 pages, 7 figures, 1 table. arXiv preprint |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.18953 [cs.LG] |
| (or arXiv:2604.18953v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.18953 arXiv-issued DOI via DataCite (pending registration) |
From: Ziming Zhou [view email]
[v1]
Tue, 21 Apr 2026 01:04:26 UTC (3,880 KB)
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