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In this thesis we study stability from several viewpoints. After covering the practical importance, the rich history and the ever-growing list of manifestations of stability, we study the following. (i) (Statistical identification of stable dynamical syste ...
Time series analysis has proven to be a powerful method to characterize several phenomena in biology, neuroscience and economics, and to understand some of their underlying dynamical features. Several methods have been proposed for the analysis of multivar ...
Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...
Measured meteorological time series are frequently used to obtain information 8 about climate dynamics. We use time series analysis and nonlinear system identification 9 methods in order to assess outdoor-environment bioclimatic conditions starting from th ...
Described is a closed-loop control scheme capable of stabilizing a parametrically excited nonlinear structure in several vibration modes. By setting the relative phase between the spatially filtered response and the excitation, the open-loop unstable solut ...
Trajectory planning through dynamical systems (DS) provides robust control for robots and has found numerous applications from locomotion to manipulation. However, to date, DS for controlling rhythmic patterns are distinct from DS used to control point to ...
Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space. The induced equivalence classes capture qualitative properties such as stability or ...
Coupled dynamical systems are omnipresent in everyday life. In general, interactions between
individual elements composing the system are captured by complex networks. The latter
greatly impact the way coupled systems are functioning and evolving in time. ...
In many tasks such as finishing operations, achieving accurate force tracking is essential. However, uncertainties in the robot dynamics and the environment limit the force tracking accuracy. Learning a compensation model for these uncertainties to reduce ...
