Learning dynamical systems via Koopman operator regression in reproducing kernel Hilbert spaces
We study a class of dynamical systems modelled as stationary Markov chains that admit an
invariant distribution via the corresponding transfer or Koopman operator. While data-driven …
invariant distribution via the corresponding transfer or Koopman operator. While data-driven …
Koopman neural operator forecaster for time-series with temporal distributional shifts
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur
in real-world time series and pose a fundamental challenge for deep neural networks …
in real-world time series and pose a fundamental challenge for deep neural networks …
Data‐driven sensor fault detection and isolation of nonlinear systems: Deep neural‐network Koopman operator
This paper proposes a data‐driven sensor fault detection and isolation approach for the
general class of nonlinear systems. The proposed method uses deep neural network …
general class of nonlinear systems. The proposed method uses deep neural network …
Learning invariant representations of time-homogeneous stochastic dynamical systems
We consider the general class of time-homogeneous stochastic dynamical systems, both
discrete and continuous, and study the problem of learning a representation of the state that …
discrete and continuous, and study the problem of learning a representation of the state that …
On the equivalence of contraction and Koopman approaches for nonlinear stability and control
B Yi, IR Manchester - IEEE Transactions on Automatic Control, 2023 - ieeexplore.ieee.org
In this paper we prove new connections between two frameworks for analysis and control of
nonlinear systems: the Koopman operator framework and contraction analysis. Each …
nonlinear systems: the Koopman operator framework and contraction analysis. Each …
Koopman-based deep iISS bilinear parity approach for data-driven fault diagnosis: Experimental demonstration using three-tank system
Precise and timely fault diagnosis is crucial in many practical systems and control
processes. Particularly due to the increasing amount of available data collected by sensors …
processes. Particularly due to the increasing amount of available data collected by sensors …
Koopman neural forecaster for time series with temporal distribution shifts
Temporal distributional shifts, with underlying dynamics changing over time, frequently occur
in real-world time series and pose a fundamental challenge for deep neural networks …
in real-world time series and pose a fundamental challenge for deep neural networks …
Multi-objective optimization of building HVAC operation: Advanced strategy using Koopman predictive control and deep learning
Predictive control is an effective method for addressing the increasing demand for heating,
ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and …
ventilation, and air conditioning (HVAC) systems to operate with greater flexibility and …
Temporal forward–backward consistency, not residual error, measures the prediction accuracy of extended dynamic mode decomposition
Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to
approximate the action of the Koopman operator on a linear function space spanned by a …
approximate the action of the Koopman operator on a linear function space spanned by a …