Results 1 to 10 of about 472,452 (242)

Learning Hamiltonian neural Koopman operator and simultaneously sustaining and discovering conservation laws

Physical Review Research
Accurately finding and predicting dynamics based on the observational data with noise perturbations is of paramount significance but still a major challenge presently.
Jingdong Zhang, Qunxi Zhu, Wei Lin
doaj   +1 more source

Heterogeneous Mixtures of Dictionary Functions to Approximate Subspace Invariance in Koopman Operators: Why Deep Koopman Operators Work

Journal of Nonlinear Science
Abstract Koopman operators model nonlinear dynamics as a linear dynamic system acting on a nonlinear function as the state. This nonstandard state is often called a Koopman observable and is usually approximated numerically by a superposition of functions drawn from a dictionary. In a widely used algorithm, extended dynamic mode decomposition
Charles A. Johnson, Shara Balakrishnan, Enoch Yeung   +2 more
openaire   +2 more sources

Data-Driven Identification of Gas Turbine Engine Dynamics via Koopman Operator Genetic Algorithm

IEEE Access
Gas turbine engines (GTEs) are highly nonlinear control-nonaffine systems. Deriving their physics-based models can be challenging, particularly when some critical parameters can be difficult to measure or determine otherwise.
David Grasev
doaj   +1 more source

Learning Neural Koopman Operators with Dissipativity Guarantees

2025 IEEE 64th Conference on Decision and Control (CDC)
We address the problem of learning a neural Koopman operator model that provides dissipativity guarantees for an unknown nonlinear dynamical system that is known to be dissipative. We propose a two-stage approach. First, we learn an unconstrained neural Koopman model that closely approximates the system dynamics.
Yuezhu Xu, S. Sivaranjani, Vijay Gupta 0001   +2 more
openaire   +4 more sources

A deep Koopman operator‐based modelling approach for long‐term prediction of dynamics with pixel‐level measurements

CAAI Transactions on Intelligence Technology
Although previous studies have made some clear leap in learning latent dynamics from high‐dimensional representations, the performances in terms of accuracy and inference time of long‐term model prediction still need to be improved. In this study, a deep
Yongqian Xiao, Zixin Tang, Xin Xu, Xinglong Zhang, Yifei Shi   +4 more
doaj   +1 more source

Optimal Control of Entity-Based Systems via Koopman Representations With Product Density Observables

IEEE Open Journal of Control Systems
The Koopman operator, which describes a dynamical system via a linear representation that can be approximately learned from data, allows application of linear control techniques to nonlinear systems.
Madeline Blischke, Joao P. Hespanha
doaj   +1 more source

Koopman fault‐tolerant model predictive control

IET Control Theory & Applications
This paper introduces a novel data‐driven approach to develop a fault‐tolerant model predictive controller (MPC) for non‐linear systems. By adopting a Koopman operator‐theoretic perspective, the proposed method leverages historical data from the system ...
Mohammadhosein Bakhtiaridoust, Meysam Yadegar, Fatemeh Jahangiri   +2 more
doaj   +1 more source

Understanding Brain Functional Dynamics Through Neural Koopman Operator With Control Mechanism. [PDF]

IEEE Trans Med Imaging
Zhou Z, Dan T, Wu G.
europepmc   +1 more source

Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation. [PDF]

IEEE Trans Neural Syst Rehabil Eng
Xue X, Ganesh V, Roque D, Garcia TP, Jiang X, Sharma N.   +5 more
europepmc   +1 more source

A Koopman operator-based prediction algorithm and its application to COVID-19 pandemic and influenza cases. [PDF]

Sci Rep
Mezić I, Drmač Z, Črnjarić N, Maćešić S, Fonoberova M, Mohr R, Avila AM, Manojlović I, Andrejčuk A.   +8 more
europepmc   +1 more source