Physic Guided Deep Learning

Physics-guided deep learning (PGDL) integrates physical laws and principles into deep learning models to improve accuracy, generalization, and efficiency, particularly when data is scarce. Current research focuses on incorporating physical constraints into neural networks through various methods, including the use of differential equations, energy-based approaches, and physics-informed loss functions, often employing architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This approach is proving valuable across diverse fields, enhancing the accuracy of simulations in areas such as structural mechanics, material science, and medical imaging, and enabling real-time applications previously limited by computational constraints. The integration of physical knowledge significantly improves model reliability and interpretability compared to purely data-driven methods.