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Gaoteng Yuan

As seen in: MDPI
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Articles

Symmetry-Preserving Physics-Informed Neural Network Framework for Relativistic Charged-Particle Dynamics in 3+1 Dimensions

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By Nikolai S. Akintsov, Artem P. Nevecheria, Gaoteng Yuan
| Preprints.org
You are already at the latest version Standard pushers for the relativistic equations of motion of a charged particle in an electromagnetic field—Boris, Vay, Higuera–Cary—do not, in general, preserve the full symplectic structure of the underlying Hamiltonian system, while high-order non-symplectic schemes such as Runge–Kutta accumulate secular error over long times.
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Research on Feature Extraction of Meteorological Disaster Emergency Response Capability Based on an RNN Autoencoder

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By Ruijia Yang, Jiansong Tang, Qiangsheng Dai, Gaoteng Yuan
| MDPI
This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by 1, 2, 3, 1 and 1,* 1 College of Computer and Information, Hohai University, Nanjing 211100, China 2 Business School, Hohai University, Nanjing 211100, China 3 State Grid Jiangsu Electric Power Company Ltd., Research Institute, Nanjing 211100, China * Author to whom correspondence should be addressed. Appl. Sci.
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