Articles | Volume 33, issue 4
https://doi.org/10.5194/npg-33-503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-33-503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Conditional updates of neural network weights for increased out of training performance
GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
Saran Rajendran Sari
GFZ Helmholtz Centre for Geosciences, Potsdam, Germany
Related authors
Reyko Schachtschneider, Jan Saynisch-Wagner, Volker Klemann, Meike Bagge, and Maik Thomas
Nonlin. Processes Geophys., 29, 53–75, https://doi.org/10.5194/npg-29-53-2022, https://doi.org/10.5194/npg-29-53-2022, 2022
Short summary
Short summary
Glacial isostatic adjustment is the delayed reaction of the Earth's lithosphere and mantle to changing mass loads of ice sheets or water. The deformation behaviour of the Earth's surface depends on the ability of the Earth's mantle to flow, i.e. its viscosity. It can be estimated from sea level observations, and in our study, we estimate mantle viscosity using sea level observations from the past. This knowledge is essential for understanding current sea level changes due to melting ice.
Reyko Schachtschneider, Jan Saynisch-Wagner, Volker Klemann, Meike Bagge, and Maik Thomas
Nonlin. Processes Geophys., 29, 53–75, https://doi.org/10.5194/npg-29-53-2022, https://doi.org/10.5194/npg-29-53-2022, 2022
Short summary
Short summary
Glacial isostatic adjustment is the delayed reaction of the Earth's lithosphere and mantle to changing mass loads of ice sheets or water. The deformation behaviour of the Earth's surface depends on the ability of the Earth's mantle to flow, i.e. its viscosity. It can be estimated from sea level observations, and in our study, we estimate mantle viscosity using sea level observations from the past. This knowledge is essential for understanding current sea level changes due to melting ice.
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Short summary
Neural networks are limited in situations that differ from the learned conditions. We propose a 3-step solution to this out of distribution problem: (1) Derive the anomalies of a trained neural networks' internal parameters by retraining on subsets of its training data. (2) Relate the ensuing network-parameter anomalies to differences in the training data subsets that caused them. (3) Extrapolate the found relations to generate a network that performs better outside the org. training distribution.
Neural networks are limited in situations that differ from the learned conditions. We propose a...