Earth System Reconstructions: Navigating data and models for digital twins
Convener:
Haipeng LiECSECS
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Co-conveners:
Guillaume Dupont-Nivet,
Christian Vérard,
Christopher Scotese
The field is witnessing significant advancements through the application of machine learning, large language models, and other sophisticated statistical and nonlinear optimization techniques. These methods enhance our ability to interpret complex and often obscure geological, environmental, and geophysical data. By integrating approaches from various disciplines, we enhance the quantifiability of geological processes over a broad spectrum of spatial and temporal scales. This integration is critical for incorporating better quantifications of uncertainty in both parameter values and model choice, as well as the fusion between geophysical, geological and environmental sensing constraints with data analyses and numerical modelling of Earth Systems.
We invite contributions from all disciplines focused on modeling or constraining Earth Systems, from deep geological times to anticipated future scenarios, whether regional or global in scope. We welcome submissions that are analytical or lab-focused, field-based, or involve numerical modelling. This session also aims to explore cutting-edge methods, tools, and approaches that push the boundaries of inference and uncertainty analysis, and interdisciplinary model-data fusion. We ask the question `Where to next?’ in our collective quest to develop digital twins of our planet.
We also celebrate the contributions of early career researchers, open/community research philosophy, and innovations that have adopted interdisciplinary approaches.