Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 22
Articles | Volume 18, issue 22
https://doi.org/10.5194/gmd-18-9219-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-9219-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 22
Development and technical paper
 | 
28 Nov 2025
Development and technical paper |  | 28 Nov 2025

Exploiting physics-based machine learning to quantify geodynamic effects – insights from the Alpine region

Denise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, and Mauro Cacace

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Short summary
Geodynamical simulations cover a wide spatial and temporal range and are crucial to understand and assess the evolution of the Earth system. To enable computationally efficient modeling approaches that can account for potentially unknown subsurface properties, we present a surrogate modeling technique. This technique combines physics-based and machine-learning techniques to enable reliable predictions of geodynamical applications, as we illustrate for the case study of the Alpine Region.
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Special issue
Advances in numerical modelling of geological processes