Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries

This article likely presents a research study on Physics-Informed Neural Networks (PINNs), focusing on their application in solving problems with specific boundary conditions, particularly in 3D geometries. The comparative aspect suggests an evaluation of different methods for enforcing these conditions within the PINN framework. The verification aspect implies the authors have validated their approach, likely against known solutions or experimental data.