9 citations as recorded by crossref.

1. 3D thermal model of Sicily (Southern Italy) and perspectives for new exploration campaigns for geothermal resources G. Floridia et al.
2. 3D multi-physics uncertainty quantification using physics-based machine learning D. Degen et al.
3. About the trustworthiness of physics-based machine learning – considerations for geomechanical applications D. Degen et al.
4. Global sensitivity analysis to optimize basin-scale conductive model calibration – A case study from the Upper Rhine Graben D. Degen et al.
5. Geothermal potential of the Czech Vienna Basin: Structural and fluid-flow dynamics of a former pull-apart basin S. Rybár et al.
6. Guidelines for Conducting Sensitivity Analyses in Process Simulations for Solid Earth Geosciences D. Degen & F. Wellmann
7. Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations D. Degen et al.
8. Sensitivity analysis using physics-based machine learning: an example from surrogate modelling for magnetotellurics N. Lindner et al.
9. Exploiting physics-based machine learning to quantify geodynamic effects – insights from the Alpine region D. Degen et al.