Combining Physics with Machine Learning to Improve Climate Modeling

Learn how to integrate physics principles with machine learning techniques to enhance climate modeling accuracy and efficiency in this conference talk that explores cutting-edge approaches to computational climate science, demonstrates practical applications of physics-informed machine learning algorithms in atmospheric and oceanic modeling, examines the challenges of incorporating physical constraints into neural networks, and discusses how hybrid physics-ML models can improve predictions of climate phenomena while maintaining computational tractability for large-scale climate simulations.