Articles | Volume 6, issue 2
https://doi.org/10.5194/gchron-6-227-2024
https://doi.org/10.5194/gchron-6-227-2024
Research article
 | 
12 Jun 2024
Research article |  | 12 Jun 2024

Solving crustal heat transfer for thermochronology using physics-informed neural networks

Ruohong Jiao, Shengze Cai, and Jean Braun

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Short summary
We demonstrate a machine learning method to estimate the temperature changes in the Earth's crust over time. The method respects physical laws and conditions imposed by users. By using observed rock cooling ages as constraints, the method can be used to estimate the tectonic and landscape evolution of the Earth. We show the applications of the method using a synthetic rock uplift model in 1D and an evolution model of a real mountain range in 3D.