7 papers
Koopman operator theory: fundamentals, control, and applications
Igor MeziÄ, Jorge Cortés, Karl Worthmann +2
The Koopman operator has gained considerable attention due to its ability to provide a global linear representation of highly complex dynamical systems. The operator describes nonl…
PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems
Ismail Hassaballa, Mircea Lazar
Hybrid models that combine physics-based and data-driven components have shown strong potential for achieving accuracy and interpretability in control applications. While recent me…
Koopman operator learning for predictive control via Khatri-Rao kernel regression
Mircea Lazar
This paper develops a data-driven realization of the generalized Koopman operator (GeKo), in which states and inputs are lifted independently and the dynamics are expressed as a te…
A Unified Representation of Neural Networks Architectures
Christophe Prieur, Mircea Lazar, Bogdan Robu
In this paper we consider the limiting case of neural networks (NNs) architectures when the number of neurons in each hidden layer and the number of hidden layers tend to infinity…
From Product Hilbert Spaces to the Generalized Koopman Operator and the Nonlinear Fundamental Lemma
Mircea Lazar
The generalization of the Koopman operator to systems with control input and the derivation of a nonlinear fundamental lemma are two open problems that play a key role in the devel…
Scalable Nonlinear DeePC: Bridging Direct and Indirect Methods and Basis Reduction
Thomas O. de Jong, Mircea Lazar, Siep Weiland +1
This paper studies regularized data-enabled predictive control (DeePC) within a nonlinear framework and its relationship to subspace predictive control (SPC). The -regularizati…