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001020503 1001_ $$0P:(DE-HGF)0$$aDegen, Denise$$b0$$eCorresponding author
001020503 245__ $$aPerspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
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001020503 520__ $$aAn 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 models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, 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 produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.
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001020503 536__ $$0G:(DE-82)EXS-PF-JARA-SDS009$$aPF-JARA-SDS009 - High Performance Computing in the Geosciences: preparation of a Research Training Group to educate the next generation of experts (EXS-PF-JARA-SDS009)$$cEXS-PF-JARA-SDS009$$x3
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001020503 7001_ $$0P:(DE-Juel1)186723$$aCaviedes Voullième, Daniel$$b1
001020503 7001_ $$00000-0002-2493-2377$$aBuiter, Susanne$$b2
001020503 7001_ $$0P:(DE-HGF)0$$aHendricks Franssen, Harrie-Jan$$b3
001020503 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b4
001020503 7001_ $$0P:(DE-Juel1)191148$$aGonzález-Nicolás, Ana$$b5$$ufzj
001020503 7001_ $$00000-0003-2552-1876$$aWellmann, Florian$$b6
001020503 773__ $$0PERI:(DE-600)2456725-5$$a10.5194/gmd-16-7375-2023$$gVol. 16, no. 24, p. 7375 - 7409$$n24$$p7375 - 7409$$tGeoscientific model development$$v16$$x1991-959X$$y2023
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