Physic Informed Deep Learning

Physics-informed deep learning (PIDL) combines the power of deep neural networks with the constraints of physical laws to solve complex scientific and engineering problems. Current research focuses on improving the accuracy and efficiency of PIDL models, exploring various architectures like convolutional and recurrent neural networks, and developing novel training strategies such as adaptive weighting and physics-guided initialization. This approach is proving valuable across diverse fields, enhancing the accuracy and efficiency of simulations in areas ranging from fluid dynamics and traffic flow estimation to material science and medical imaging. The integration of physical knowledge into deep learning models leads to more robust, reliable, and interpretable results compared to purely data-driven approaches.