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Data-driven soliton mappings for integrable fractional nonlinear wave equations via deep learning with Fourier neural operator 期刊论文
CHAOS SOLITONS & FRACTALS, 2022, 卷号: 165, 页码: 14
作者:  Zhong, Ming;  Yan, Zhenya
收藏  |  浏览/下载：67/0  |  提交时间：2023/02/07
Integrable fractional nonlinear wave equations  Fourier neural operator  Deep learning  Data-driven soliton mapping  Activation function  Channel of fully-connected layer  
Multi-scale analysis-driven tourism forecasting: insights from the peri-COVID-19 期刊论文
CURRENT ISSUES IN TOURISM, 2022, 页码: 24
作者:  Li, Mingchen;  Zhang, Chengyuan;  Wang, Shouyang;  Sun, Shaolong
收藏  |  浏览/下载：61/0  |  提交时间：2023/02/07
Tourism demand forecasting  divide and conquer  Facebook prophet  deep learning  peri-COVID-19 era  
Data-driven rogue waves and parameters discovery in nearly integrable PT-symmetric Gross-Pitaevskii equations via PINNs deep learning 期刊论文
PHYSICA D-NONLINEAR PHENOMENA, 2022, 卷号: 439, 页码: 12
作者:  Zhong, Ming;  Gong, Shibo;  Tian, Shou-Fu;  Yan, Zhenya
收藏  |  浏览/下载：67/0  |  提交时间：2023/02/07
GeneralizedGrossPitaevskiiequation  ComplexPT-symmetricpotentials  Physics-informeddeepneuralnetworks  Data-driven rogue waves and parameters discovery discovery  
Data-driven discoveries of B?cklund transformations and soliton evolution equations via deep neural network learning schemes 期刊论文
PHYSICS LETTERS A, 2022, 卷号: 450, 页码: 15
作者:  Zhou, Zijian;  Wang, Li;  Yan, Zhenya
收藏  |  浏览/下载：72/0  |  提交时间：2023/02/07
Deep neural networks  B?cklund transform  Miura transform  Soliton equations  Breathers  Solitons  
Data-Driven Deep Learning for The Multi-Hump Solitons and Parameters Discovery in NLS Equations with Generalized PT-Scarf-II Potentials 期刊论文
NEURAL PROCESSING LETTERS, 2022, 页码: 19
作者:  Zhong, Ming;  Zhang, Jian-Guo;  Zhou, Zijian;  Tian, Shou-Fu;  Yan, Zhenya
收藏  |  浏览/下载：53/0  |  提交时间：2023/02/07
Focusing and defocusing nonlinear Schrodinger equations  Generalized PT-Scarf-II potential  Physics-informed deep neural networks  Data-driven solitons and parameters discovery  

Normalizing field flows: Solving forward and inverse stochastic differential equations using physics-informed flow models

 期刊论文
JOURNAL OF COMPUTATIONAL PHYSICS, 2022, 卷号: 461, 页码: 18
作者:  Guo, Ling;  Wu, Hao;  Zhou, Tao
收藏  |  浏览/下载：117/0  |  提交时间：2023/02/07
Data -driven modeling  Normalizing flows  Uncertainty quantification  Random fields  
An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting 期刊论文
APPLIED ENERGY, 2022, 卷号: 306, 页码: 16
作者:  Yang, Dongchuan;  Guo, Ju-E;  Sun, Shaolong;  Han, Jing;  Wang, Shouyang
收藏  |  浏览/下载：117/0  |  提交时间：2022/04/02
Short-term load forecasting  Bivariate empirical mode decomposition  Decomposition-ensemble approach  Reconstruction  Bayesian optimization  Long short-term memory network  
AN ORTHOGONALIZATION-FREE PARALLELIZABLE FRAMEWORK FOR ALL-ELECTRON CALCULATIONS IN DENSITY FUNCTIONAL THEORY 期刊论文
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2022, 卷号: 44, 期号: 3, 页码: B723-B745
作者:  Gao, Bin;  Hu, Guanghu;  Kuang, Yang;  Liu, Xin
收藏  |  浏览/下载：99/0  |  提交时间：2023/02/07
Key words  density functional theory  all-electron calculations  total energy minimization  orthogonalization-free  scalability  
Data-driven peakon and periodic peakon solutions and parameter discovery of some nonlinear dispersive equations via deep learning 期刊论文
PHYSICA D-NONLINEAR PHENOMENA, 2021, 卷号: 428, 页码: 15
作者:  Wang, Li;  Yan, Zhenya
收藏  |  浏览/下载：116/0  |  提交时间：2022/04/02
Nonlinear dispersive equation  Initial-boundary value conditions  Physics-informed neural networks  Deep learning  Data-driven peakon and periodic peakon  solutions Data-driven parameter discovery  
On the small time asymptotics of scalar stochastic conservation laws 期刊论文
APPLICABLE ANALYSIS, 2021, 页码: 25
作者:  Dong, Zhao;  Zhang, Rangrang
收藏  |  浏览/下载：118/0  |  提交时间：2022/04/02
Small time asymptotic  large deviations  scalar stochastic conservation laws