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pro vyhledávání: '"Moradi, Mohammadamin"'
Deep learning models have revolutionized various domains, with Multi-Layer Perceptrons (MLPs) being a cornerstone for tasks like data regression and image classification. However, a recent study has introduced Kolmogorov-Arnold Networks (KANs) as pro
Externí odkaz:
http://arxiv.org/abs/2410.02077
Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mat
Externí odkaz:
http://arxiv.org/abs/2409.15167
Autor:
Panahi, Shirin, Kong, Ling-Wei, Moradi, Mohammadamin, Zhai, Zheng-Meng, Glaz, Bryan, Haile, Mulugeta, Lai, Ying-Cheng
Anticipating a tipping point, a transition from one stable steady state to another, is a problem of broad relevance due to the ubiquity of the phenomenon in diverse fields. The steady-state nature of the dynamics about a tipping point makes its predi
Externí odkaz:
http://arxiv.org/abs/2402.14877
Publikováno v:
APL Machine Learning 2 (1), 016118 (2024)
It was recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth's anomaly magnetic field immersed in overwhelming complex signals for ma
Externí odkaz:
http://arxiv.org/abs/2402.14131
Complex and nonlinear dynamical systems often involve parameters that change with time, accurate tracking of which is essential to tasks such as state estimation, prediction, and control. Existing machine-learning methods require full state observati
Externí odkaz:
http://arxiv.org/abs/2311.09142
Autor:
Zhai, Zheng-Meng, Moradi, Mohammadamin, Kong, Ling-Wei, Glaz, Bryan, Haile, Mulugeta, Lai, Ying-Cheng
Publikováno v:
Nat Commun 14, 5698 (2023)
Nonlinear tracking control enabling a dynamical system to track a desired trajectory is fundamental to robotics, serving a wide range of civil and defense applications. In control engineering, designing tracking control requires complete knowledge of
Externí odkaz:
http://arxiv.org/abs/2309.11470
Autor:
Zhai, Zheng-Meng1, Moradi, Mohammadamin1, Panahi, Shirin1, Wang, Zhi-Hua2, Lai, Ying-Cheng1,3 Ying-Cheng.Lai@asu.edu
Publikováno v:
APL Machine Learning. Sep2024, Vol. 2 Issue 3, p1-15. 15p.
Autor:
Moradi, Mohammadamin1, Panahi, Shirin1, Zhai, Zheng-Meng1, Weng, Yang1, Dirkman, John2, Lai, Ying-Cheng1,3 Ying-Cheng.Lai@asu.edu
Publikováno v:
APL Machine Learning. Jun2024, Vol. 2 Issue 2, p1-16. 16p.
Akademický článek
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Autor:
Moradi M; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA., Zhai ZM; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA., Panahi S; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA., Lai YC; School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.; Department of Physics, Arizona State University, Tempe, Arizona 85287, USA.
Publikováno v:
Chaos (Woodbury, N.Y.) [Chaos] 2024 Dec 01; Vol. 34 (12).