Discovering dependencies in complex physical systems using Neural Networks
This addresses the challenge of identifying relationships in data-scarce, non-linear systems, which is incremental as it builds on existing mutual information and neural network techniques.
The paper tackled the problem of discovering non-linear dependencies in complex dynamical systems like weather forecasting and econometric models, proposing a method based on mutual information and deep neural networks that can find relationships even with small datasets.
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.