Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 24
Articles | Volume 16, issue 24
https://doi.org/10.5194/gmd-16-7375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-7375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 24
Review and perspective paper
 | 
19 Dec 2023
Review and perspective paper |  | 19 Dec 2023

Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations

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

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Editorial statement
This manuscript provides a review of physics-based machine learning methods, and provides a perspective on their use.
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Short summary
In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
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