Physics-Guided Machine Learning for Earth System Modeling

Learn the fundamentals of physics-guided machine learning in this comprehensive tutorial delivered by Tom Beucler at the International Centre for Theoretical Sciences. Explore how to integrate physical principles and domain knowledge into machine learning models to create more robust and interpretable solutions for scientific applications. Discover the key concepts behind physics-informed neural networks (PINNs) and understand how to incorporate physical constraints, conservation laws, and differential equations into ML architectures. Examine practical approaches for combining data-driven methods with established physical theories to improve model accuracy and generalizability. Gain insights into the advantages of physics-guided approaches over purely data-driven methods, including better performance with limited data, improved extrapolation capabilities, and enhanced interpretability. Understand the mathematical foundations underlying physics-informed learning and learn how to implement these techniques in real-world scientific computing scenarios. This tutorial is part of the Advanced Machine Learning for Earth System Modeling program, making it particularly relevant for researchers working on climate modeling, environmental sciences, and computational physics applications.