Physic Informed Learning

Physics-informed learning (PIL) integrates physical laws and principles directly into machine learning models, aiming to improve accuracy, data efficiency, and generalization capabilities compared to purely data-driven approaches. Current research emphasizes the development and application of various neural network architectures, including Physics-Informed Neural Networks (PINNs), graph neural networks, and transformer-enhanced networks, to solve partial differential equations and model complex physical systems across diverse domains like seismology, robotics, and fluid dynamics. This interdisciplinary field is significantly impacting scientific modeling by enabling more accurate and efficient simulations with limited data, leading to advancements in areas such as weather forecasting, materials science, and engineering design.