Reply on AC2

Abstract

Abstract. An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate 5 models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable and robust models. In this manuscript, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced basis method as a physics-based machine learning approach is able to 10 produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.

Authors 7

1. Denise Degen corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Computational Geoscience, Geothermics and Reservoir Geophysics (CGGR), Mathieustraße 30, 52074 Aachen, Germany

2. Daniel Caviedes Voullià ̈me

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Agrosphere, IBG-3, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

   Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Simulation and Data Lab. Terrestrial Systems, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

3. Susanne Buiter Aachen

   RWTH Aachen University · GFZ Helmholtz Centre for Geosciences

   Affiliation as printed

   Helmholtz Centre Potsdam – GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany

   RWTH Aachen University, Tectonics and Geodyamics (TAG), Lochnerstraße 4-20, 52064 Aachen, Germany

4. Harrie-Jan Hendriks Franssen

   Forschungszentrum Jülich

   Affiliation as printed

   Centre for High Performance Computing Terrestrial Systems, Geoverbund ABC/J, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

   Forschungszentrum Jülich GmbH, Agrosphere, IBG-3, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

5. Harry Vereecken

   Forschungszentrum Jülich

   Affiliation as printed

   Centre for High Performance Computing Terrestrial Systems, Geoverbund ABC/J, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

   Forschungszentrum Jülich GmbH, Agrosphere, IBG-3, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

6. Ana González-Nicolás

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Forschungszentrum Jülich GmbH, Jülich Supercomputing Centre (JSC), Simulation and Data Lab. Terrestrial Systems, Wilhelm-Johnen-Straße, 52425 Jülich, Germany

7. Florian Wellmann Aachen

   RWTH Aachen University · Fraunhofer Research Institution for Energy Infrastructures and Geotechnologies IEG

   Affiliation as printed

   Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems (IEG), Am Hochschulcampus 1, 44801 Bochum, Germany

   RWTH Aachen University, Computational Geoscience, Geothermics and Reservoir Geophysics (CGGR), Mathieustraße 30, 52074 Aachen, Germany