Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 13, issue 7
Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-3373-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-3373-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 7
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

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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.
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