Physics-constrained neural networks for surrogate modeling of lossless periodic structures
This episode explores a paper by Eric Prehn and Peter Jung on physics-constrained neural networks (PCNN) for modeling periodic structures. The hosts discuss how PCNN uses mathematical constraints to ensure energy conservation, offering a much faster and more reliable alternative to traditional RCWA simulations for designing complex technologies like augmented reality waveguides.