Signal propagation in complex networks drives epidemics, is responsible for information
going viral, promotes trust and facilitates moral behavior in social groups, enables the …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

The Koopman operator allows for handling nonlinear systems through a globally linear
representation. In general, the operator is infinite-dimensional–necessitating finite …

Recurrent neural networks are widely used on time series data, yet such models often
ignore the underlying physical structures in such sequences. A new class of physics-based …

Skeleton-based human action recognition is becoming increasingly important in a variety of
fields. Most existing works train a CNN or GCN based backbone to extract spatial-temporal …

The Koopman operator has emerged as a powerful tool for the analysis of nonlinear
dynamical systems as it provides coordinate transformations to globally linearize the …

Finding an embedding space for a linear approximation of a nonlinear dynamical system
enables efficient system identification and control synthesis. The Koopman operator theory …

Autonomous driving technologies have received notable attention in the past decades. In
autonomous driving systems, identifying a precise dynamical model for motion control is …

The Koopman operator theory linearly describes nonlinear dynamical systems in a high-
dimensional functional space and it allows to apply linear control methods to highly …