Prof. Dr. Kristian Kersting

Abstract

We present a framework for incorporating physical constraints into deep learning models for climate prediction. By embedding conservation laws, symmetry principles, and thermodynamic constraints directly into the network architecture, we achieve 40% reduction in prediction error for regional climate projections while maintaining physical consistency. The approach is demonstrated on precipitation forecasting, wind energy prediction, and extreme weather event detection across European regions.