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@ARTICLE{Degen:1020503,
      author       = {Degen, Denise and Caviedes Voullième, Daniel and Buiter,
                      Susanne and Hendricks Franssen, Harrie-Jan and Vereecken,
                      Harry and González-Nicolás, Ana and Wellmann, Florian},
      title        = {{P}erspectives of physics-based machine learning strategies
                      for geoscientific applications governed by partial
                      differential equations},
      journal      = {Geoscientific model development},
      volume       = {16},
      number       = {24},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2024-00222},
      pages        = {7375 - 7409},
      year         = {2023},
      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 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.},
      cin          = {IBG-3 / JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118 / I:(DE-Juel1)JSC-20090406},
      pnm          = {5121 - Supercomputing $\&$ Big Data Facilities (POF4-512) /
                      5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 2A5 - Exascale Earth
                      System Modeling (CARF - CCA) (POF4-2A5) / PF-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) / 2173 - Agro-biogeosystems:
                      controls, feedbacks and impact (POF4-217)},
      pid          = {G:(DE-HGF)POF4-5121 / G:(DE-HGF)POF4-5111 /
                      G:(DE-HGF)POF4-2A5 / G:(DE-82)EXS-PF-JARA-SDS009 /
                      G:(DE-HGF)POF4-2173},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001168854000001},
      doi          = {10.5194/gmd-16-7375-2023},
      url          = {https://juser.fz-juelich.de/record/1020503},
}