Data Driven Model

Data-driven models leverage large datasets and machine learning algorithms to build predictive models of complex systems, aiming to improve accuracy and efficiency compared to traditional methods. Current research focuses on enhancing model interpretability through techniques like Koopman operator estimation and physics-informed machine learning (PIML), as well as addressing challenges such as data scarcity via self-supervised learning and transfer learning, and mitigating issues of overfitting and instability through regularization. These advancements are significantly impacting diverse fields, from weather forecasting and materials science to hydrology and healthcare, by enabling more accurate predictions and improved decision-making in data-rich environments.