Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems

• URL: http://arxiv.org/abs/2004.06243v3
• Date: Tue, 31 Aug 2021 03:03:46 GMT
• Title: Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems
• Authors: Priyabrata Saha, Saurabh Dash, Saibal Mukhopadhyay
• Abstract summary: In this paper, we present a hybrid framework combining numerical physics-based models with deep learning for source identification. We formulate our model PhICNet as a convolutional recurrent neural network (RNN) which is end-to-end trainable for predicting S-temporal evolution. Experimental results show that the proposed model can forecast the dynamics for a relatively long time and identify the sources as well.
• Score: 10.689157154434499
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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