Below are key pieces of information for the course (WINTER 2020).
Introduces fundamental concepts of network science and graph theory for complex dynamical systems. Merges concepts from model selection, information theory, statistical inference, neural networks, deep learning and machine learning for building reduced order models of dynamical systems using sparse sampling of high-dimensional data.
Exploratory and objective data analysis methods applied to the physical, engineering and biological sciences. Brief overview of statistical methods and their computational implementation for time series analysis, spectral analysis, filtering methods, principal component analysis, proper orthogonal decompositions, and image processing and compression.