Climate modeling with AI: Hype or Reality? & Deep learning and the dynamics of physical processes
0Climate modeling with AI: Hype or Reality?\n\nClimate 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. \n\nDeep learning and the dynamics of physical processes\n\nDeep 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.\n\nSpeakers:\n\nLaure Zanna\nCourant Institute, New York University\n\nPatrick Gallinari\nSorbonne University\nModerator(s):\n\nMarkus Reichstein\nMax Planck Institute for Biogeochemistry\n\nJoin the Neural Network! \nhttps://aiforgood.itu.int/neural-network/\nThe AI for Good networking community platform powered by AI. \nDesigned to help users build connections with innovators and experts, link innovative ideas with social impact opportunities, and bring the community together to advance the SDGs using AI.\n\n Watch the latest #AIforGood videos!\n\n\nExplore more #AIforGood content:\n AI for Good Top Hits\n • Top Hits \n\n AI for Good Webinars\n • AI for Good Webinars \n\n AI for Good Keynotes\n • AI for Good Keynotes \n\n Stay updated and join our weekly AI for Good newsletter:\nhttp://eepurl.com/gI2kJ5\n\n Discover what's next on our programme!\nhttps://aiforgood.itu.int/programme/\n\nCheck out the latest AI for Good news:\nhttps://aiforgood.itu.int/newsroom/\n\nExplore the AI for Good blog:\nhttps://aiforgood.itu.int/ai-for-good-blog/\n\n Connect on our social media:\nWebsite: https://aiforgood.itu.int/\nTwitter: https://twitter.com/AIforGood\nLinkedIn Page: https://www.linkedin.com/company/26511907 \nLinkedIn Group: https://www.linkedin.com/groups/8567748 \nInstagram: https://www.instagram.com/aiforgood \nFacebook: https://www.facebook.com/AIforGood\n\nWhat is AI for Good?\nWe have less than 10 years to solve the UN SDGs and AI holds great promise to advance many of the sustainable development goals and targets.\nMore than a Summit, more than a movement, AI for Good is presented as a year round digital platform where AI innovators and problem owners learn, build and connect to help identify practical AI solutions to advance the United Nations Sustainable Development Goals.\nAI for Good is organized by ITU in partnership with 40 UN Sister Agencies and co-convened with Switzerland.\n\nDisclaimer:\nThe views and opinions expressed are those of the panelists and do not reflect the official policy of the ITU.