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

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

doi:10.5194/gmd-18-9219-2025

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:

Advances in numerical modelling of geological processes

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

Data sets

Non-Intrusive Reduced Basis Method - Case Study of the Alpine Region Denise Degen et al. https://doi.org/10.5281/zenodo.14755256

LaMEM and ASPECT input and data files corresponding to Exploiting Physics-Based Machine Learning to Quantify Geodynamic Effects – Insights from the Alpine Region Ajay Kumar https://doi.org/10.5281/zenodo.17051562

Pre-post process scripts and LaMEM input files to generate data for Surrogate Models Ajay Kumar https://doi.org/10.5281/zenodo.16640814

LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling Ajay Kumar https://doi.org/10.5281/zenodo.7071571

Model code and software

geodynamics/aspect: ASPECT 2.5.0 (v2.5.0) Wolfgang Bangerth et al. https://doi.org/10.5281/zenodo.8200213

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.