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Page 1
Approximation capabilities of measure-preserving neural networks.
Neural Netw. 2022 Mar;147:72-80. doi: 10.1016/j.neunet.2021.12.007. Epub 2021 Dec 21.
Neural Netw. 2022.
PMID: 34995951
Finite Difference Method for Time-Space Fractional Advection-Diffusion Equations with Riesz Derivative.
Arshad S, Baleanu D, Huang J, Al Qurashi MM, Tang Y, Zhao Y.
Arshad S, et al. Among authors: tang y.
Entropy (Basel). 2018 Apr 26;20(5):321. doi: 10.3390/e20050321.
Entropy (Basel). 2018.
PMID: 33265411
Free PMC article.
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Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness.
Jin P, Lu L, Tang Y, Karniadakis GE.
Jin P, et al. Among authors: tang y.
Neural Netw. 2020 Oct;130:85-99. doi: 10.1016/j.neunet.2020.06.024. Epub 2020 Jul 3.
Neural Netw. 2020.
PMID: 32650153
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Explicit symplectic algorithms based on generating functions for charged particle dynamics.
Zhang R, Qin H, Tang Y, Liu J, He Y, Xiao J.
Zhang R, et al. Among authors: tang y.
Phys Rev E. 2016 Jul;94(1-1):013205. doi: 10.1103/PhysRevE.94.013205. Epub 2016 Jul 21.
Phys Rev E. 2016.
PMID: 27575228
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The graphical representation of protein sequences based on the physicochemical properties and its applications.
He PA, Zhang YP, Yao YH, Tang YF, Nan XY.
He PA, et al.
J Comput Chem. 2010 Aug;31(11):2136-42. doi: 10.1002/jcc.21501.
J Comput Chem. 2010.
PMID: 20225279
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SympNets: Intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems.
Jin P, Zhang Z, Zhu A, Tang Y, Karniadakis GE.
Jin P, et al. Among authors: tang y.
Neural Netw. 2020 Dec;132:166-179. doi: 10.1016/j.neunet.2020.08.017. Epub 2020 Aug 27.
Neural Netw. 2020.
PMID: 32890788
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