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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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GMD | Articles | Volume 13, issue 7
Geosci. Model Dev., 13, 3373–3382, 2020
https://doi.org/10.5194/gmd-13-3373-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 3373–3382, 2020
https://doi.org/10.5194/gmd-13-3373-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 30 Jul 2020

Model description paper | 30 Jul 2020

PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations

Olivier Pannekoucke and Ronan Fablet

Model code and software

opannekoucke/pdenetgen: pde-netgen-GMD (Version 1.0.1) O. Pannekoucke https://doi.org/10.5281/zenodo.3891101

PDE-NetGen Source code O. Pannekoucke https://github.com/opannekoucke/pdenetgen

Publications Copernicus
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Short summary
Learning physics from data using a deep neural network is a challenge that requires an appropriate but unknown network architecture. The package introduced here helps to design an architecture by translating known physical equations into a network, which the experimenter completes to capture unknown physical processes. A test bed is introduced to illustrate how this learning allows us to focus on truly unknown physical processes in the hope of making better use of data and digital resources.
Learning physics from data using a deep neural network is a challenge that requires an...
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