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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Cited articles

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X.: TensorFlow: large-scale machine learning on heterogeneous systems, arXiv [preprint], https://doi.org/10.48550/arXiv.1603.04467, 16 March 2016. a, b
Boster, K. A., Cai, S., Ladrón-de Guevara, A., Sun, J., Zheng, X., Du, T., Thomas, J. H., Nedergaard, M., Karniadakis, G. E., and Kelley, D. H.: Artificial intelligence velocimetry reveals in vivo flow rates, pressure gradients, and shear stresses in murine perivascular flows, P. Natl. Acad. Sci. USA, 120, e2217744120, https://doi.org/10.1073/pnas.2217744120, 2023. a
Brandon, M. T., Roden-Tice, M. K., and Carver, J. I.: Late Cenozoic exhumation of the Cascadia accretionary wedge in the Olympic Mountains, northwest Washington State, Bull. Geol. Soc. Am., 110, 985–1009, https://doi.org/10.1130/0016-7606(1998)110<0985:LCEOTC>2.3.CO;2, 1998. a, b, c
Braun, J.: Pecube: a new finite-element code to solve the 3D heat transport equation including the effects of a time-varying, finite amplitude surface topography, Comput. Geosci., 29, 787–794, https://doi.org/10.1016/S0098-3004(03)00052-9, 2003. a, b, c, d, e
Braun, J. and Robert, X.: Constraints on the rate of post-orogenic erosional decay from low-temperature thermochronological data: Application to the Dabie Shan, China, Earth Surf. Proc. Land., 30, 1203–1225, https://doi.org/10.1002/esp.1271, 2005. a, b, c, d
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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.