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Refinado por: autor: Liu, Wei remover
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1
Some New Optical Solitons of the Generalized Radhakrishnan–Kundu–Lakshmanan Equations with Powers of Nonlinearity
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Some New Optical Solitons of the Generalized Radhakrishnan–Kundu–Lakshmanan Equations with Powers of Nonlinearity

Chen, Cheng ; Li, Lu ; Liu, Wei

Symmetry (Basel), 2022-12, Vol.14 (12), p.2626 [Periódico revisado por pares]

Basel: MDPI AG

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2
Averaging principle for slow-fast stochastic differential equations with time dependent locally Lipschitz coefficients
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Averaging principle for slow-fast stochastic differential equations with time dependent locally Lipschitz coefficients

Liu, Wei ; Röckner, Michael ; Sun, Xiaobin ; Xie, Yingchao

Journal of Differential Equations, 2020-03, Vol.268 (6), p.2910-2948 [Periódico revisado por pares]

Elsevier Inc

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3
Stationary distribution of the stochastic theta method for nonlinear stochastic differential equations
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Artigo
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Stationary distribution of the stochastic theta method for nonlinear stochastic differential equations

Jiang, Yanan ; Weng, Lihui ; Liu, Wei

Numerical algorithms, 2020-04, Vol.83 (4), p.1531-1553 [Periódico revisado por pares]

New York: Springer US

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4
Truncated Euler-Maruyama method for classical and time-changed non-autonomous stochastic differential equations
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Truncated Euler-Maruyama method for classical and time-changed non-autonomous stochastic differential equations

Liu, Wei ; Mao, Xuerong ; Tang, Jingwen ; Wu, Yue

Applied numerical mathematics, 2020-07, Vol.153, p.66-81 [Periódico revisado por pares]

Elsevier B.V

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5
McKean–Vlasov SDE and SPDE with locally monotone coefficients
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McKean–Vlasov SDE and SPDE with locally monotone coefficients

Hong, Wei ; Hu, Shanshan ; Liu, Wei

The Annals of applied probability, 2024-04, Vol.34 (2), p.2136 [Periódico revisado por pares]

Hayward: Institute of Mathematical Statistics

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6
Self-adaptive physical information neural network model for prediction of two-phase flow annulus pressure
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Self-adaptive physical information neural network model for prediction of two-phase flow annulus pressure

Xu, Baochang ; Zhang, Xuezhi ; Wang, Yaxin ; Liu, Wei ; Meng, Zhuoran

Shíyóu xuébào, 2023-01, Vol.44 (3), p.545

Beijing: Shiyou Xuebao

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7
An Analytical Dynamic Model for Vibration Suppression of a Multi-Span Continuous Bridge by Tuned Mass Dampers
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An Analytical Dynamic Model for Vibration Suppression of a Multi-Span Continuous Bridge by Tuned Mass Dampers

Wei, Jin ; Liu, Wei ; Gao, Peixin ; Ding, Yujie

Journal of marine science and engineering, 2023-05, Vol.11 (5), p.1017 [Periódico revisado por pares]

Basel: MDPI AG

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8
Dynamics of diverse data-driven solitons for the three-component coupled nonlinear Schrödinger model by the MPS-PINN method
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Dynamics of diverse data-driven solitons for the three-component coupled nonlinear Schrödinger model by the MPS-PINN method

Wen, Xue-Kun ; Wu, Gang-Zhou ; Liu, Wei ; Dai, Chao-Qing

Nonlinear dynamics, 2022-09, Vol.109 (4), p.3041-3050 [Periódico revisado por pares]

Dordrecht: Springer Netherlands

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9
Equivalence of the mean square stability between the partially truncated Euler–Maruyama method and stochastic differential equations with super-linear growing coefficients
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Equivalence of the mean square stability between the partially truncated Euler–Maruyama method and stochastic differential equations with super-linear growing coefficients

Jiang, Yanan ; Huang, Zequan ; Liu, Wei

Advances in difference equations, 2018-10, Vol.2018 (1), p.1-15, Article 355 [Periódico revisado por pares]

Cham: Springer International Publishing

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10
Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures
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Neural modal ordinary differential equations: Integrating physics-based modeling with neural ordinary differential equations for modeling high-dimensional monitored structures

Lai, Zhilu ; Liu, Wei ; Jian, Xudong ; Bacsa, Kiran ; Sun, Limin ; Chatzi, Eleni

Data-Centric Engineering (Online), 2022-01, Vol.3, Article e34 [Periódico revisado por pares]

Cambridge: Cambridge University Press

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