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Physics-incorporated convolutional recurrent neural networks for source identification and forecasting of dynamical systems.
- Source :
-
Neural Networks . Dec2021, Vol. 144, p359-371. 13p. - Publication Year :
- 2021
-
Abstract
- Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs). Though the core dynamics follows some principles of physics, real-world physical processes are often driven by unknown external sources. In such cases, developing a purely analytical model becomes very difficult and data-driven modeling can be of assistance. In this paper, we present a hybrid framework combining physics-based numerical models with deep learning for source identification and forecasting of spatio-temporal dynamical systems with unobservable time-varying external sources. We formulate our model PhICNet as a convolutional recurrent neural network (RNN) which is end-to-end trainable for spatio-temporal evolution prediction of dynamical systems and learns the source behavior as an internal state of the RNN. Experimental results show that the proposed model can forecast the dynamics for a relatively long time and identify the sources as well. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08936080
- Volume :
- 144
- Database :
- Academic Search Index
- Journal :
- Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 153338291
- Full Text :
- https://doi.org/10.1016/j.neunet.2021.08.033