Physic Embedded

Physics-embedded machine learning integrates physical principles into deep learning models to improve accuracy, generalizability, and efficiency in various applications. Current research focuses on incorporating physics-based constraints and features within neural networks, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformers, for tasks ranging from 3D image reconstruction and LiDAR simulation to cloth simulation and fluid dynamics modeling. This approach leverages prior knowledge to overcome limitations of purely data-driven methods, leading to more robust and reliable models with improved performance and reduced computational costs. The resulting advancements have significant implications for diverse fields, including biomedical imaging, autonomous driving, and computer graphics.