Articles | Volume 13, issue 7
https://doi.org/10.5194/gmd-13-3373-2020
https://doi.org/10.5194/gmd-13-3373-2020
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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Cited articles

Auer, M., Tschurtschenthaler, T., and Biffl, S.: A Flyweight UML Modelling Tool for Software Development in Heterogeneous Environments, in: Proceedings of the 29th Conference on EUROMICRO, EUROMICRO '03, 267 pp., IEEE Computer Society, Washington, DC, USA, https://doi.org/10.5555/942796.943259, 2003. a
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Cai, J.-F., Dong, B., Osher, S., and Shen, Z.: Image restoration: Total variation, wavelet frames, and beyond, J. Am. Math. Soc., 25, 1033–1089, https://doi.org/10.1090/s0894-0347-2012-00740-1, 2012. a
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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.