FlowMLLab: An open-source framework for reproducible computational-fluid-dynamics and scientific-machine-learning experiments

Ehsan Roohi

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arXiv · 2609.27975

FlowMLLab: An open-source framework for reproducible computational-fluid-dynamics and scientific-machine-learning experiments

Ehsan Roohi

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Abstract

FlowMLLab is an open-source framework that connects validated fluid-mechanics solvers to scientific machine-learning experiments under a reproducible contract. Its computational fluid dynamics (CFD) pathway generates cavity data, whereas its direct simulation Monte Carlo (DSMC) pathway supplies rarefied-flow evidence with sampling diagnostics. Case-wise partitions prevent leakage, and matched non-neural baselines determine whether learning offers a measurable advantage. Examples include a coordinate network and a proper orthogonal decomposition (POD) deep operator network (DeepONet), together with learned kinetic closures. Python components regenerate every figure, while sixteen tutorials use the same interfaces as the command-line tools. By retaining numerical provenance, blind-case protocols and physical validation targets, FlowMLLab emphasizes traceable evidence rather than black-box curve fitting.

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Ehsan Roohi. 2026-08-24. FlowMLLab: An open-source framework for reproducible computational-fluid-dynamics and scientific-machine-learning experiments. https://arxiv.org/abs/2609.27975

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