Physic Guided Architecture

Physics-guided architectures integrate physical principles into artificial neural networks, aiming to improve model accuracy, interpretability, and training efficiency. Current research explores various architectures, including those based on physical models like the Cook-Torrance reflectance model for image processing and building thermal dynamics for energy modeling, often employing techniques like transfer learning and denoising autoencoders to enhance performance. This approach is significant because it leverages prior knowledge to address challenges such as limited data, model overfitting, and the "black box" nature of many deep learning models, leading to more reliable and explainable results across diverse applications.