Explore cutting-edge applications of machine learning in high-performance climate science, focusing on data post-processing, compression techniques, and Earth virtualization engines. Delve into advanced topics presented by experts from the Scalable Parallel Computing Lab at ETH Zurich in this comprehensive 68-minute conference talk. Gain insights into how ML is revolutionizing climate data handling, enhancing computational efficiency, and enabling more accurate Earth system simulations. Learn about innovative approaches to tackle the challenges of processing and storing massive climate datasets, and discover how Earth virtualization engines are transforming our ability to model and understand complex climate systems.
ML for High-Performance Climate: Data Post Processing, Compression, and Earth Virtualization Engines
Scalable Parallel Computing Lab, SPCL @ ETH Zurich via YouTube
Overview
Syllabus
ML for High-Performance Climate: Data Post Processing, Compression, and Earth Virtualization Engines
Taught by
Scalable Parallel Computing Lab, SPCL @ ETH Zurich
Related Courses
-
Introduction to High-Performance and Parallel Computing
-
Earth Virtualization Engines (EVE) for Climate Change Modeling
-
Data Analytics and Machine Learning Innovation for Climate and Earth Surface Processes
-
Advancing Weather and Climate Prediction with Data-Driven Methods
-
Designing High-Performance Scalable Middleware for HPC, AI, and Data Science in Exascale Systems and Clouds
-
TinyML Talks Germany - Neural Network Framework Using Emerging Technologies for Screening Diabetic
