Results 41 to 50 of about 903 (207)
Neural Koopman forecasting for critical transitions in infrastructure networks
Intelligent Systems with ApplicationsWe develop a data-driven framework for long-term forecasting of stochastic dynamics on evolving networked infrastructure systems using neural approximations of Koopman operators.
Ramen Ghosh
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Weighted Frobenius-Perron operators and their spectra [PDF]
Mathematica Bohemica, 2017 First, some classic properties of a weighted Frobenius-Perron operator $\mathcal{P}_\varphi^u$ on $L^1(\Sigma)$ as a predual of weighted Koopman operator $W=uU_\varphi$ on $L^\infty(\Sigma)$ will be investigated using the language of the conditional ...
Mohammad Reza Jabbarzadeh +1 more
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Does Participating in Agricultural Global Value Chains Promote Agricultural Growth?
Agribusiness, EarlyView.ABSTRACT This study examines the relationship between GVC participation and agricultural value‐added growth in 43 countries over the period 1995–2022. In contrast to prior literature, we disaggregate the agricultural sector into four sub‐sectors namely crop cultivation, animal production, forestry and fishing.
Taner Turan +2 more
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Decompositions of Dynamical Systems Induced by the Koopman Operator [PDF]
Analysis Mathematica, 2021 For a topological dynamical system we characterize the decomposition of the state space induced by the fixed space of the corresponding Koopman operator. For this purpose, we introduce a hierarchy of generalized orbits and obtain the finest decomposition of the state space into absolutely Lyapunov stable sets. Analogously to the measure-preserving case,
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IEEE AccessShort-term voltage stability (STVS) prediction is a critical technology for modern power systems with high penetration of renewable energy resources. To address the limitations of the traditional maximum Lyapunov exponent (MLE) in handling short-time ...
Han Gao +3 more
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Advanced Intelligent Discovery, EarlyView.The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
Stabilizing RED Using the Koopman Operator
IEEE Signal Processing LettersThe widely used RED (Regularization-by-Denoising) framework uses pretrained denoisers as implicit regularizers for model-based reconstruction. Although RED generally yields high-fidelity reconstructions, the use of black-box denoisers can sometimes lead to instability.
Shraddha Chavan, Kunal N. Chaudhury
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Dynamical systems and complex networks: a Koopman operator perspective
Journal of Physics: ComplexityThe Koopman operator has entered and transformed many research areas over the last years. Although the underlying concept—representing highly nonlinear dynamical systems by infinite-dimensional linear operators—has been known for a long time, the ...
Stefan Klus, Nataša Djurdjevac Conrad
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Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
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Koopman-Based Control System for Quadrotors in Noisy Environments
IEEE AccessIt is well known that identification of the complete system dynamics is challenging, especially in noisy environments. The Koopman operator theory provides a linear representation of a nonlinear system using only the input/output data acquired from the ...
Yuna Oh, Myoung Hoon Lee, Jun Moon
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