Physics-informed machine learning is a research area that combines principles of physics with machine learning techniques to improve the accuracy and efficiency of models used in various scientific and engineering applications. By incorporating physical laws and constraints into the machine learning algorithms, researchers can develop models that are more interpretable, generalize better to unseen data, and have improved robustness. This interdisciplinary approach has been applied to a wide range of problems such as fluid dynamics, materials science, and climate modeling, leading to advancements in predictive capabilities and scientific understanding.