Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
<p>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 spac...
Main Authors: | D. Degen, D. Caviedes Voullième, S. Buiter, H.-J. Hendricks Franssen, H. Vereecken, A. González-Nicolás, F. Wellmann |
---|---|
Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2023-12-01
|
Series: | Geoscientific Model Development |
Online Access: | https://gmd.copernicus.org/articles/16/7375/2023/gmd-16-7375-2023.pdf |
Similar Items
-
Computational advances and innovations in flood risk mapping
by: Michael Nones, et al.
Published: (2020-12-01) -
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
by: T. Boas, et al.
Published: (2023-08-01) -
Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications
by: L. Strebel, et al.
Published: (2022-01-01) -
Is high-resolution inverse characterization of heterogeneous river bed hydraulic conductivities needed and possible?
by: W. Kurtz, et al.
Published: (2013-10-01) -
Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0
by: T. Boas, et al.
Published: (2021-01-01)