Luis A. de la Fuente

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

This work presents NeuralFlowNet, a data-free, physics-informed proof-of-concept framework designed to solve steady Navier-Stokes problems across low- to high-Reynolds-number conditions, and establishes NeuralFlowNet as a reliable framework for future testing of unsteady and more complex simulations.