Physic Informed

Physics-informed machine learning integrates physical principles and domain knowledge into machine learning models to improve accuracy, efficiency, and interpretability, particularly when data is scarce or complex systems are involved. Current research focuses on developing and applying physics-informed neural networks (PINNs), DeepONets, and other architectures to diverse problems, including solving partial differential equations, modeling physical systems (e.g., fluid dynamics, structural mechanics), and improving AI reasoning. This approach is significantly impacting various fields by enabling faster, more accurate simulations, enhanced model generalization, and the development of more robust and reliable AI systems for scientific discovery and engineering applications.