Integrating Physics, Data and Scientific Machine Learning to Predict Climate Variability and Extremes

Explore a comprehensive seminar on integrating physics, data, and scientific machine learning to predict climate variability and extremes, presented by Prof. Pedram Hassanzadeh from Rice University. Delve into cutting-edge approaches that combine traditional physics-based models with advanced data analysis and machine learning techniques to enhance our understanding and prediction of complex climate phenomena. Gain insights into how these interdisciplinary methods are revolutionizing climate science and improving our ability to forecast extreme weather events and long-term climate patterns.