Accelerating the Discovery of Steady‐States of Planetary Interior Dynamics With Machine Learning

Abstract Simulating mantle convection often requires reaching a computationally expensive steady‐state, crucial for deriving scaling laws for thermal and dynamical flow properties and benchmarking numerical solutions. The strong temperature dependence of the rheology of mantle rocks causes viscosity...

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Bibliographic Details
Main Authors: Siddhant Agarwal, Nicola Tosi, Christian Hüttig, David S. Greenberg, Ali Can Bekar
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Journal of Geophysical Research: Machine Learning and Computation
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Online Access:https://doi.org/10.1029/2024JH000438
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