Autoregressive graph Volterra models and applications

Graph-based learning and estimation are fundamental problems in various applications involving power, social, and brain networks, to name a few. While learning pair-wise interactions in network data is a well-studied problem, discovering higher-order interactions among subsets of nodes is still not yet fully explored. To this end, encompassing...

Non-Linear Bayesian System Identification of Cortical Responses Using Volterra Series

The human sensorimotor system can be seen as a complex network in which the brain plays an important role, resulting in a difficult-to-understand relation between proprioceptive stimuli and cortical responses. However, understanding this relationship is of added value for understanding various diseases which cause dysfunctionality. In recent...

Self-Driven Graph Volterra Models for Higher-Order Link Prediction

Link prediction is one of the core problems in network and data science with widespread applications. While predicting pairwise nodal interactions (links) in network data has been investigated extensively, predicting higher-order interactions (higher-order links) is still not fully understood. Several approaches have been advocated to predict...

Added damping of a wind turbine rotor: Two-dimensional discretization expressing the nonlinear wind-force dependency

In determining wind forces on wind turbine blades, and subsequently on the tower and the foundation, the blade response velocity cannot be neglected. This velocity alters the wind force, which depends on the wind velocity relative to that of the blades This blade response velocity component of the wind force is commonly referred to as added...