Since 2020, aggregated from related topics
Physics-informed modeling is a research area that combines physics-based models with data-driven approaches to predict complex systems and phenomena. This approach involves incorporating prior knowledge of the underlying physical laws and equations governing a system into machine learning algorithms, allowing for more accurate predictions while also capturing uncertainties and incorporating data-driven insights. Physics-informed modeling is commonly used in fields such as fluid dynamics, materials science, and climate modeling, where accurate and interpretable models are crucial for decision-making and understanding complex systems.