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.

The authors acknowledge funding by Shell-Stanford, which might raise potential conflicts of interest regarding the application of the research to oil and gas industry problems. However, the paper itself addresses a general methodology in computational fluid dynamics, and no specific bias towards Shell's interests is evident in the problem selection or results presented.

The paper presents a novel approach to solving PDEs on irregular geometries using physics-informed deep learning. Combining PointNet's ability to capture geometric features with the physics-informed framework is a significant advancement. The methodology demonstrates strong potential for accelerating computational physics, particularly in design optimization where exploring various geometries is crucial. The comprehensive results and error analysis further strengthen the paper. However, limiting the scope to steady-state flows and lacking a thorough comparison with other advanced PINN architectures prevents a perfect 5 rating.