Applications of physics-informed machine learning in accelerating dynamical models of permafrost processes ...
<!--!introduction!--> Perennially frozen ground, typically referred to as permafrost, plays a significant role in Arctic environments. Monitoring its response to rapid climate change is challenging, however, due to the limited availability of high quality, long-term observations of subsurface...
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| Format: | Conference Object |
| Language: | unknown |
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GFZ German Research Centre for Geosciences
2023
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| Online Access: | https://dx.doi.org/10.57757/iugg23-1900 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017665 |
| Summary: | <!--!introduction!--> Perennially frozen ground, typically referred to as permafrost, plays a significant role in Arctic environments. Monitoring its response to rapid climate change is challenging, however, due to the limited availability of high quality, long-term observations of subsurface hydrothermal conditions, e.g. measurements of soil temperature and moisture. Numerical models are thus an indispensable tool for understanding how permafrost is changing at larger scales, but simulating the hydrothermal processes impacting it is difficult due to the nonlinear effects of phase change in porous media. The computational cost of such simulations is prohibitive, particularly for sensitivity analysis and parameter estimation tasks which require a large number of simulations with different parameter settings. To address this issue, we examine possible applications of physics-informed machine learning (PIML) methods in improving and accelerating dynamical models of permafrost processes. We assess the ... : The 28th IUGG General Assembly (IUGG2023) (Berlin 2023) ... |
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