AI 中文总结
NeuralFlowNet是一种无数据物理信息神经网络框架，可在不依赖外部流场数据的情况下，准确求解低至高雷诺数下的稳态纳维-斯托克斯方程，为开发高雷诺数流体动力学AI求解器提供了可靠基础。
AI 中文摘要
物理信息神经网络（PINNs）已成为实现基于人工智能的可靠计算流体动力学（CFD）的重要途径，其核心是将控制方程直接嵌入学习过程。现有多数AI流动模型依赖大量模拟或实验数据集，且往往仅适用于特定问题，限制了其泛化能力与物理可靠性。无数据PINN框架提供了另一种思路，可通过学习纳维-斯托克斯方程及规定边界条件的流动解，减少对昂贵CFD数据集的依赖，同时保持物理一致性。然而，传统PINN框架在高雷诺数场景下表现不佳，制约了其在CFD中的应用。本研究提出NeuralFlowNet，这是一种无数据、物理信息的概念验证框架，旨在求解低至高雷诺数范围内的稳态纳维-斯托克斯问题。我们描述了所提方法框架，并通过物理与几何复杂度递增的基准问题验证其适用性。结果表明，NeuralFlowNet无需在外部流场数据上训练，即可准确恢复宽雷诺数范围内的稳态流场，包括存在强压力梯度的情况，且与参考数值解吻合良好。这些发现确立了NeuralFlowNet作为可靠框架，可用于未来非稳态及更复杂模拟的测试，并为开发高雷诺数流体动力学领域可靠高效的AI求解器奠定基础。
英文摘要
Physics-informed neural networks (PINNs) have emerged as a compelling pathway toward trustworthy artificial-intelligence-based computational fluid dynamics (CFD) by embedding governing equations directly into the learning process. Many existing AI flow models require large simulation or experimental datasets and often remain problem-specific, limiting their generalization and physical reliability. Data-free PINN frameworks offer an alternative by learning flow solutions from the Navier-Stokes equations and prescribed boundary conditions, potentially reducing dependence on expensive CFD datasets while retaining physical consistency. However, traditional PINN frameworks have struggled at high Reynolds numbers, limiting their application in CFD. In this work, we present NeuralFlowNet, a data-free, physics-informed proof-of-concept framework designed to solve steady Navier-Stokes problems across low- to high-Reynolds-number conditions. We describe the proposed methodological framework and demonstrate its applicability using benchmark problems with increasing physical and geometric complexity. The results demonstrate that NeuralFlowNet can accurately recover steady flow fields across a broad Reynolds-number range, including cases with strong pressure gradients, without training on external flow-field data and while maintaining good agreement with reference numerical solutions. These findings establish NeuralFlowNet as a reliable framework for future testing of unsteady and more complex simulations and could provide a basis for developing trustworthy and efficient AI solvers for high-Reynolds-number fluid dynamics.
