{"id":2617,"date":"2025-01-17T09:49:41","date_gmt":"2025-01-17T08:49:41","guid":{"rendered":"https:\/\/wp.unil.ch\/iaunil\/glaciers-in-motion-how-ai-reconstructs-their-past-and-forecasts-their-future\/"},"modified":"2025-12-10T13:45:13","modified_gmt":"2025-12-10T12:45:13","slug":"glaciers-in-motion-how-ai-reconstructs-their-past-and-forecasts-their-future","status":"publish","type":"post","link":"https:\/\/wp.unil.ch\/iaunil\/en\/glaciers-in-motion-how-ai-reconstructs-their-past-and-forecasts-their-future\/","title":{"rendered":"Glaciers in motion: how AI reconstructs their past and forecasts their future"},"content":{"rendered":"\n

Original text published on https:\/\/wp.unil.ch\/geoblog\/2024\/03\/modeliser-levolution-des-glaciers-grace-au-machine-learning\/<\/a><\/em><\/p>\n\n

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Understanding glaciers means delving into both Earth\u2019s ancient history and its climatic future. Guillaume Jouvet, a glaciologist and mathematician at UNIL, leverages machine learning to refine models of glacier dynamics. Using the power of GPUs and deep learning, he simulates glacier evolution over millennia with increasingly high resolution. But AI isn\u2019t just a research tool\u2014it\u2019s also a medium for outreach, capable of generating striking visualizations of Alpine landscapes as they appeared during glaciations. This is science at the intersection of cutting-edge technology and imagination.


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The integration of artificial intelligence into his research aims to unlock new avenues for exploring glacier dynamics, understanding their historical role in shaping our landscapes, anticipating their future evolution, and assessing potential impacts on sectors such as alpine tourism and risk management.<\/p>\n\n\n\n

Modeling glacier dynamics is a complex task that involves solving equations incorporating a wide range of parameters\u2014climatic, physical, and geomorphological. Traditionally, this approach was constrained by computational limitations, due to the sheer volume of data and variables involved. Deep learning has enabled Guillaume Jouvet to harness the full computing power at his disposal and optimize the efficiency of his modeling calculations (see inset). <\/p>\n<\/div>\n\n\n\n

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Switching from a scale of kilometres to a scale of hundreds of metres<\/h3>\n\n\n\n

In the context of glacier dynamics, AI allows Guillaume Jouvet to revolutionize his modeling approaches\u2014both in terms of temporal depth and spatial resolution. One of his goals is to enhance the resolution of a simulation tracing the evolution of Alpine glacier coverage over 120,000 years, which he and his colleagues initially developed using a classical model. <\/p>\n\n\n\n

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