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

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Sep 2025	3,488	20	6	3,514
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Cumulative views and downloads (calculated since 30 May 2025)

Month	HTML	PDF	XML	Total
May 2025	70	10	4	84
Jun 2025	200	42	12	254
Jul 2025	490	58	28	576
Aug 2025	974	20	4	998
Sep 2025	3,488	20	6	3,514
Oct 2025	172	32	4	208
Nov 2025	151	59	9	219
Dec 2025	262	118	21	401
Jan 2026	625	54	23	702
Feb 2026	425	126	24	575
Mar 2026	175	130	17	322
Apr 2026	146	83	6	235
May 2026	132	71	5	208
Jun 2026	22	20	6	48
Jul 2026	38	35	5	78
Aug 2026	72	32	7	111
Sep 2026	206	62	67	335
Oct 2026	92	37	17	146

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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.