Data-driven Modeling

Catalog description

Introduction of fundamental mathematical techniques and theories for data-driven modeling, with a focus on applications to agricultural sciences, biological systems engineering, and related areas. Topics include dimensionality reduction and coordinate transformation, sparse modeling, modeling time-series data, and machine learning. Common application areas encompass pattern extraction from spatiotemporal data profiles, determination of optimal sensor displacement, robust image reconstruction, reduced-order modeling, and more.