Physics-informed PointNet: A deep learning solver for steady-state incompressible flows and thermal fields on multiple sets of irregular geometries
This paper introduces Physics-Informed PointNet (PIPN), a deep learning solver that predicts fluid flow and thermal fields on multiple sets of irregular geometries. PIPN uses a point-cloud neural network to handle irregular shapes and physics-informed learning to capture the underlying physics, allowing it to be trained on various geometries simultaneously and generalize to unseen shapes from different categories.