Physics-informed machine learning

Physics-informed machine learning is a method that embeds physical laws and engineering principles into machine learning models, aiming to enhance the prediction accuracy and generalization capability of these models. By integrating data-driven approaches with physical knowledge, this method can provide more reliable solutions in modeling complex systems, especially in scenarios where data is scarce or the physical processes are intricate. Not only does it accelerate model training, but it also ensures that the model's predictions adhere to physical rules, thereby demonstrating significant application value in scientific research and engineering applications.