Search Results for author:
Found 5 papers, 0 papers with code
Data-driven Influence Based Clustering of Dynamical Systems
Community detection is a challenging and relevant problem in various disciplines of science and engineering like power systems, gene-regulatory networks, social networks, financial networks, astronomy etc.
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach
In recent years data-driven analysis of dynamical systems has attracted a lot of attention and transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being used almost ubiquitously.
Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms
While Granger Causality helped identify the most influential factors affecting crash severity, the learning-based models predicted the severity classes with varying performance.
Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic
The variables selected by Granger causality were used to train the three different reduced order models by ordinary least square regression, Ridge regression, and LASSO regression algorithms.
Information Based Data-Driven Characterization of Stability and Influence in Power Systems
In this paper, we provide a purely data-driven technique for small-signal stability classification (voltage or angle stability) and influence characterization for a power network.
