Discovering dependencies in complex physical systems using Neural Networks

• URL: http://arxiv.org/abs/2101.12583v1
• Date: Wed, 27 Jan 2021 18:59:19 GMT
• Title: Discovering dependencies in complex physical systems using Neural Networks
• Authors: Sachin Kasture
• Abstract summary: A method based on mutual information and deep neural networks is proposed as a versatile framework for discovering non-linear relationships. We demonstrate the application of this method to actual multivariable non-linear dynamical systems.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In todays age of data, discovering relationships between different variables is an interesting and a challenging problem. This problem becomes even more critical with regards to complex dynamical systems like weather forecasting and econometric models, which can show highly non-linear behavior. A method based on mutual information and deep neural networks is proposed as a versatile framework for discovering non-linear relationships ranging from functional dependencies to causality. We demonstrate the application of this method to actual multivariable non-linear dynamical systems. We also show that this method can find relationships even for datasets with small number of datapoints, as is often the case with empirical data.

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