Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Mulugeta Haile"'
Autor:
Zheng-Meng Zhai, Mohammadamin Moradi, Ling-Wei Kong, Bryan Glaz, Mulugeta Haile, Ying-Cheng Lai
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract 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 kno
Externí odkaz:
https://doaj.org/article/52701337be3c4c5993deb1adab474f6a
Publikováno v:
Physical Review Research, Vol 6, Iss 1, p 013196 (2024)
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:
https://doaj.org/article/ecba93c1c688458eb258c15dd9cfde28
Publikováno v:
IEEE Access, Vol 10, Pp 56550-56563 (2022)
Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. Howe
Externí odkaz:
https://doaj.org/article/469452653cba4ec8835fcf08e5954e07
Publikováno v:
Physical Review Research, Vol 3, Iss 2, p 023156 (2021)
The rapid growth of research in exploiting machine learning to predict chaotic systems has revived a recent interest in Hamiltonian neural networks (HNNs) with physical constraints defined by Hamilton's equations of motion, which represent a major cl
Externí odkaz:
https://doaj.org/article/1ad9f1e96c1841ec9004359107f8e6ec
Autor:
Ying-Cheng Lai, Ling-Wei Kong, Zheng-Meng Zhai, Mohammadamin Moradi, Bryan Glaz, Mulugeta Haile
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:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e99ca7d02cef7990f05433f7ba6130d
https://doi.org/10.21203/rs.3.rs-2886752/v1
https://doi.org/10.21203/rs.3.rs-2886752/v1
In our work, we developed a pattern-producing neural network that we named Transitional Pattern Producing Networks (TPPN). The TPPN generates soft robot morphologies much faster than existing methods. Our TPPN architecture uses only sine activation f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ffd093ee607c93172f357a18c8d88e5
https://doi.org/10.36227/techrxiv.20436018
https://doi.org/10.36227/techrxiv.20436018
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse. We demonst
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f94c4259809a12da7e0d27c4b16aedae
https://doi.org/10.21203/rs.3.rs-1871210/v1
https://doi.org/10.21203/rs.3.rs-1871210/v1
Publikováno v:
Chaos: An Interdisciplinary Journal of Nonlinear Science. 33:033111
We articulate the design imperatives for machine learning based digital twins for nonlinear dynamical systems, which can be used to monitor the “health” of the system and anticipate future collapse. The fundamental requirement for digital twins o
Publikováno v:
Mechanical Systems and Signal Processing. 187:109949
Publikováno v:
2019 22th International Conference on Information Fusion (FUSION).