Physics-informed learning is a research area that combines traditional physics-based models with machine learning algorithms to improve the accuracy and efficiency of predictive modeling in physics-based systems. By integrating physics principles into the machine learning process, researchers aim to learn from data more effectively while ensuring that the resulting predictive models adhere to physical rules and constraints. This approach can help enhance the predictive capabilities of models in various scientific and engineering fields, such as fluid dynamics, materials science, and climate modeling. By leveraging the strengths of both physics-based modeling and machine learning, physics-informed learning has the potential to accelerate scientific discovery and innovation in complex systems.