NeuralFlowNet: Towards Data-Free Physics-Informed Neural Network Solutions of Navier-Stokes Equations Across Low and High Reynolds Numbers

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