FlowMLLab：一个用于可复现计算流体动力学与科学机器学习实验的开源框架
FlowMLLab: An open-source framework for reproducible computational-fluid-dynamics and scientific-machine-learning experiments
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中文总结 AI 辅助
FlowMLLab是一个开源框架，连接验证的流体力学求解器与科学机器学习实验，通过可复现契约、数据分区和基线比较，强调可追溯证据而非黑盒拟合。
中文摘要 AI 辅助
FlowMLLab是一个开源框架，在可复现契约下将经过验证的流体力学求解器与科学机器学习实验相连接。其计算流体动力学（CFD）路径生成空腔数据，而其直接模拟蒙特卡洛（DSMC）路径则提供带有采样诊断的稀薄流证据。按案例划分防止数据泄漏，匹配的非神经网络基线用于确定学习是否能带来可衡量的优势。示例包括坐标网络和本征正交分解（POD）深度算子网络（DeepONet），以及学习到的动力学闭合模型。Python组件可重新生成每个图表，而十六个教程使用与命令行工具相同的接口。通过保留数值来源、盲测协议和物理验证目标，FlowMLLab强调可追溯的证据而非黑盒曲线拟合。
英文摘要
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.
发表机构
- University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
机构由 AI 辅助整理，请以论文原文为准。
