Results 71 to 80 of about 780 (160)

Transfer operators on graphs: spectral clustering and beyond

Journal of Physics: Complexity
Graphs and networks play an important role in modeling and analyzing complex interconnected systems such as transportation networks, integrated circuits, power grids, citation graphs, and biological and artificial neural networks.
Stefan Klus, Maia Trower
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Predicting the unseen: improving robustness in koopman surrogate models for crowd dynamics at a bottleneck

Acta Polytechnica CTU Proceedings
Crowds moving through bottlenecks form a dynamical system, with its density fluctuating in time and space. The system dynamics can be learned and predicted using the Koopman operator framework.
Sabrina Kern, Gerta Köster
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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
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Deep Embedding Koopman Neural Operator-Based Nonlinear Flight Training Trajectory Prediction Approach

Mathematics
Accurate flight training trajectory prediction is a key task in automatic flight maneuver evaluation and flight operations quality assurance (FOQA), which is crucial for pilot training and aviation safety management. The task is extremely challenging due
Jing Lu, Jingjun Jiang, Yidan Bai
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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
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Koopman Operator Based Modeling and Control of Quadrotors

This book showcases a collection of papers that present cutting-edge studies, methods, experiments, and applications in various interdisciplinary fields. These fields encompass optimal control, guidance, navigation, game theory, stability, nonlinear dynamics, robotics, sensor fusion, machine learning, and autonomy. The chapters reveal novel studies and
Simone Martini, Alessandro Rizzo, Margareta Stefanovic, Patrizia Livreri, Matthew J. Rutherford, Kimon P. Valavanis   +5 more
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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
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Nonparametric Sparse Online Learning of the Koopman Operator

CoRR
This work was intended as a replacement of arXiv:2405.07432 and any subsequent updates will appear ...
Hou, Boya, Sanjari, Sina, Dahlin, Nathan, Koppel, Alec, Bose, Subhonmesh   +4 more
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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
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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
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