Physic Informed Convolutional Neural Network

Physics-informed convolutional neural networks (PICNNs) integrate physical laws into the training of convolutional neural networks (CNNs), improving accuracy and interpretability while maintaining the efficiency of deep learning approaches. Current research focuses on applying PICNNs to diverse problems, including real-time optimization, dynamical systems analysis, and solving partial differential equations (PDEs), often employing U-Net architectures or iterative neural networks. This approach offers significant advantages in various fields by enabling faster, more accurate solutions to complex problems while enhancing the explainability of the resulting models compared to traditional CNNs.