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An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations

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Published in:Earth Surface Dynamics (ESurf)
Format: Online Article RSS Article
Published: 2026
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container_title Earth Surface Dynamics (ESurf)
description
discipline_display Physical Sciences
discipline_facet Physical Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:42047
institution FRELIP
journal_source_facet Earth Surface Dynamics (ESurf)
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
Earth Sciences
Earth Sciences
Physical Sciences
sub_discipline_display Earth Sciences
sub_discipline_facet Earth Sciences
subject_display Earth Sciences
Earth Sciences
Physical Sciences
Earth Sciences
Earth Sciences
Physical Sciences
subject_facet Earth Sciences
Earth Sciences
Physical Sciences
title An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
title_auth An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
title_full An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
title_fullStr An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
title_full_unstemmed An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
title_short An integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
title_sort an integrated deep learning framework enables rapid spatiotemporal morphodynamic predictions toward long-term simulations
topic Earth Sciences
Earth Sciences
Physical Sciences
url https://doi.org/10.5194/esurf-14-313-2026