Climate modeling with AI: Hype or Reality? & Deep learning and the dynamics of physical processes
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Climate modeling with AI: Hype or Reality?
Climate simulations remain one of the best tools to understand and predict global and regional climate change. Uncertainties in climate predictions originate partly from the poor or lacking representation of processes, such as ocean turbulence and clouds, that are not resolved in global climate models but impact the large-scale temperature, rainfall, sea level, etc. The representation of these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning (ML) algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty? This talk discusses the advantages and challenges of using machine learning for climate projections. The focus will be on recent work in which we leverage ML tools to learn representations of unresolved ocean processes – in particular, learning symbolic expression. Some of this work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections.
Deep learning and the dynamics of physical processes
Deep learning has been studied for a few years for the modeling of complex physical processes in industrial fields such as aeronautics or energy production and in scientific fields such as environment or health. This area of research, although still emerging, is rapidly gaining momentum and developing as an interdisciplinary field. It raises new challenges for the interaction between machine learning and physics. This talk will focus on deep learning approaches for modeling dynamic physical systems and illustrate three main challenges: incorporating prior physical knowledge into learning models, generalizing learning models to multiple environments, and learning models operating continuously in space and time thus allowing flexible extrapolation at arbitrary spatiotemporal locations. This presentation will be illustrated by applications in different domains.
This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.