Results 41 to 50 of about 894 (220)

Deep learning for Koopman operator approximations for control

, 2021
The focus of this thesis is on combining the potential of Koopman operator theory with that of deep learning. This research topic has gained more and more interest in the scientific community in the last few years and is appealing for its great potential
Hader, Magdalena
core   +1 more source

Decentralized Stability Enhancement of DFIG-Based Wind Farms in Large Power Systems: Koopman Theoretic Approach

IEEE Access, 2022
This paper proposes a data-centric model predictive control (MPC) for supplemental control of a DFIG-based wind farm (WF) to improve power system stability.
Ahmed Husham, Innocent Kamwa, M. A. Abido, Hussein Supreme   +3 more
doaj   +1 more source

Reduced-order modelling based on Koopman operator theory

CoRR
The present study focuses on a subject of significant interest in fluid dynamics: the identification of a model with decreased computational complexity from numerical code output using Koopman operator theory. A reduced-order modelling method that incorporates a novel strategy for identifying the most impactful Koopman modes was used to numerically ...
Diana Alina Bistrian, Gabriel Dimitriu, Ionel Michael Navon   +2 more
openaire   +2 more sources

Randomized Projection Learning Method for Dynamic Mode Decomposition

Mathematics, 2021
A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on a projected space. In the spirit of Johnson–Lindenstrauss lemma, we will use a random projection to estimate the DMD modes in a reduced ...
Sudam Surasinghe, Erik M. Bollt
doaj   +1 more source

Modeling of Nonlinear Dynamical Systems Using Koopman Operator Theory

, 2021
The dynamics of most mechanical systems tends to incorporate nonlinearfunctions and behaviors to model complex systems. Due to the complexity of some of these systems, only analytical solutions can be found to model, optimize and control them however the
Snyder, Gregory Alonzo
core  

Koopman-Based Control System for Quadrotors in Noisy Environments

IEEE Access
It 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
doaj   +1 more source

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, Halit Yanıkkaya, Hüseyin Alperen Özer   +2 more
wiley   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

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, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

Neural Koopman forecasting for critical transitions in infrastructure networks

Intelligent Systems with Applications
We 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
doaj   +1 more source