NeuralGCM

What should Earth system modeling, and computational science more generally, look like in the era of deep learning and generative AI? In this talk, I’ll describe our lessons from building NeuralGCM, our physics- and AI-based atmospheric model written in Python and JAX. I’ll explain the fundamental advantages of AI-based approaches, where they fall short, and how they can be effectively composed with physics-based models. I’ll also show how Google’s JAX framework is an incredibly powerful platform for building computational models. Stephan Hoyer is a Staff Software Engineer at Google Research, where he leads a team building AI-based weather and climate models. His research spans the intersection of physics, numerical computing and machine learning. Stephan has made significant contributions to open source libraries for scientific computing in Python, including Xarray, NumPy and JAX. He holds a Ph.D in Physics from the University of California, Berkeley. NeuralGCM: https://github.com/neuralgcm/neuralgcm Join the GDG AI for Science community for talks, events, workshops, collaborations and more: https://gdg.community.dev/gdg-ai-for-science-australia/