Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 33, issue 4
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.
https://doi.org/10.5194/npg-33-503-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 33, issue 4
Research article
 | 
02 Oct 2026
Research article |  | 02 Oct 2026

Conditional updates of neural network weights for increased out of training performance

Jan Saynisch-Wagner and Saran Rajendran Sari

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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.
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