New Publication by Dr. Denise Degen on 'Geoscientific Model Development'

 

Perspectives of Physics-Based Machine Learning Strategies for Geoscientific Applications Governed by Partial Differential Equations

Authors: Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann


Journal: Geoscientific Model Development


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 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 produce highly precise surrogate models that are explainable, scalable, interpretable, and robust.

For more information, please visit the GMD website. An open preprint version of the paper is also available.

  Perspectives of Physics-Based Machine Learning Strategies for Geoscientific Applications Governed by Partial Differential Equations Urheberrecht: © CG3
08.11.2023