Knowledge-based Deep Learning for Modeling Chaotic Systems

• URL: http://arxiv.org/abs/2209.04259v1
• Date: Fri, 9 Sep 2022 11:46:25 GMT
• Title: Knowledge-based Deep Learning for Modeling Chaotic Systems
• Authors: Zakaria Elabid, Tanujit Chakraborty, Abdenour Hadid
• Abstract summary: This paper considers extreme events and their dynamics and proposes models based on deep neural networks, called knowledge-based deep learning (KDL) Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data. We validate our model by assessing it on three real-world benchmark datasets: El Nino sea surface temperature, San Juan Dengue viral infection, and Bjornoya daily precipitation.
• Score: 7.075125892721573
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics problems and predicting nonlinear dynamical systems. However, modeling and forecasting the dynamics of chaotic systems remains an open research problem since training deep learning models requires big data, which is not always available in many cases. Such deep learners can be trained from additional information obtained from simulated results and by enforcing the physical laws of the chaotic systems. This paper considers extreme events and their dynamics and proposes elegant models based on deep neural networks, called knowledge-based deep learning (KDL). Our proposed KDL can learn the complex patterns governing chaotic systems by jointly training on real and simulated data directly from the dynamics and their differential equations. This knowledge is transferred to model and forecast real-world chaotic events exhibiting extreme behavior. We validate the efficiency of our model by assessing it on three real-world benchmark datasets: El Nino sea surface temperature, San Juan Dengue viral infection, and Bj{\o}rn{\o}ya daily precipitation, all governed by extreme events' dynamics. Using prior knowledge of extreme events and physics-based loss functions to lead the neural network learning, we ensure physically consistent, generalizable, and accurate forecasting, even in a small data regime.

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