Computational Electromagnetics
Published:
Explored the application of Physics-Informed Neural Networks (PINNs) for solving second-order elliptic PDEs in quantum systems, particularly focusing on eigenvalue problems. PINNs were employed to model quantum phenomena, leveraging the network’s ability to approximate solutions to PDEs while respecting the underlying physics. This approach is well-suited for solving eigenvalue problems in quantum mechanics, providing a flexible framework for capturing complex behaviors in systems governed by elliptic PDEs. Github Repository