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Fourth-Order Conservative Non-splitting Semi-Lagrangian Hermite WENO Schemes for Kinetic and Fluid Simulations
期刊论文
Journal of Scientific Computing,2024, 卷号: 99, 期号: 3
作者:
Zheng, Nanyi
;
Cai, Xiaofeng
;
Qiu, Jing Mei
;
Qiu, Jianxian
Adobe PDF(5615Kb)
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收藏
  |  
浏览/下载:44/21
  |  
提交时间:2024/06/24
Conservative semi-Lagrangian
HWENO reconstruction
Incompressible flows
Non-splitting scheme
Positivity preservation
Vlasov systems
Recent Progress of Ion-Modified TiO2 for Enhanced Photocatalytic Hydrogen Production
评论文章
2024
作者:
Zhao, Dongqiu
;
Tang, Xiao
;
Liu, Penglan
;
Huang, Qiao
;
Li, Tingxian
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2024/09/04
dopants
hydrogen evolution reaction
photocatalysts
TiO2
water splitting
Comparison of Semi-Lagrangian Discontinuous Galerkin Schemes for Linear and Nonlinear Transport Simulations
期刊论文
Communications on Applied Mathematics and Computation,2022, 卷号: 4, 期号: 1, 页码: 3–33
作者:
Cai, Xiaofeng
;
Guo, Wei
;
Qiu, Jing-Mei
收藏
  |  
浏览/下载:16/0
  |  
提交时间:2022/05/10
Semi-Lagrangian (SL)
Transport simulations
Splitting
Non-splitting
Comparison
Discontinuous Galerkin (DG)
A fourth-order conservative semi-Lagrangian finite volume WENO scheme without operator splitting for kinetic and fluid simulations
期刊论文
Computer Methods in Applied Mechanics and Engineering,2022, 卷号: 395
作者:
Zheng, Nanyi
;
Cai, Xiaofeng
;
Qiu, Jing-Mei
;
Qiu, Jianxian
收藏
  |  
浏览/下载:15/0
  |  
提交时间:2022/05/10
Vlasov systems
Non-splitting scheme
Semi-Lagrangian
WENO reconstruction
Mass conservation
High-order accuracy
Unsupervised Learning Using Variational Inference on Finite Inverted Dirichlet Mixture Models with Component Splitting
期刊论文
Wireless Personal Communications,2021, 卷号: 119, 期号: 2, 页码: 1817-1844
作者:
Maanicshah, Kamal
;
Amayri, Manar
;
Bouguila, Nizar
;
Fan, Wentao
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2025/07/15
Component splitting
Inverted Dirichlet distribution
Mixture models
Unsupervised learning
Variational inference
EFFICIENT ONLINE LABEL CONSISTENT HASHING FOR LARGE-SCALE CROSS-MODAL RETRIEVAL
会议论文
Proceedings - IEEE International Conference on Multimedia and Expo, Shenzhen, 5-9 July 2021
作者:
Yi, Jinhan
;
Liu, Xin
;
Cheung, Yiuming
;
Xu, Xing
;
Fan, Wentao
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2025/07/15
forward-backward splitting
mini-batch online gradient descent
Online label consistent hashing
online semantic representation
On accurately resolving detonation dynamics by adaptive finite volume method on unstructured grids
期刊论文
Communications in Computational Physics,2020, 卷号: 29, 期号: 2, 页码: 445-471
作者:
Di, Yana
;
Hu, Guanghui
;
Li, Ruo
;
Yang, Feng
收藏
  |  
浏览/下载:29/0
  |  
提交时间:2021/04/23
H-adaptive methods
Reactive Euler equations
Strang splitting scheme
Subsonic outflow boundary
ZND model
Variational Learning for Finite Generalized Inverted Dirichlet Mixture Models with a Component Splitting Approach
会议论文
IEEE International Symposium on Industrial Electronics, Vancouver, CANADA, JUN 12-14, 2019
作者:
Maanicshah, Kamal
;
Bouguila, Nizar
;
Fan, Wentao
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2025/07/15
Component Splitting
Mixture Models
Unsupervised Learning
Variational Inference
A High Order Semi-Lagrangian Discontinuous Galerkin Method for the Two-Dimensional Incompressible Euler Equations and the Guiding Center Vlasov Model Without Operator Splitting
期刊论文
Journal of Scientific Computing,2019, 卷号: 79, 期号: 2, 页码: 1111-1134
作者:
Cai, Xiaofeng
;
Guo, Wei
;
Qiu, Jing-Mei
收藏
  |  
浏览/下载:27/0
  |  
提交时间:2022/05/10
Adaptive time-stepping method
Discontinuous Galerkin
Guiding center Vlasov model
Incompressible Euler equations
Mass conservative
Non-splitting
Semi-Lagrangian
Unsupervised variational learning of finite generalized inverted dirichlet mixture models with feature selection and component splitting
会议论文
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Waterloo, CANADA, AUG 27-29, 2019
作者:
Maanicshah,Kamal
;
Ali, Samr
;
Fan, Wentao
;
Bouguila, Nizar
收藏
  |  
浏览/下载:8/0
  |  
提交时间:2025/07/15
Component splitting
Feature selection
Generalized inverted Dirichlet
Unsupervised learning
Variational learning