What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Artificial intelligence
- Algorithm
Mathematical analysis, Multigrid method, Linear system, Discretization and Convection–diffusion equation are his primary areas of study.
Jun Zhang has included themes like Mathematical optimization and Relaxation in his Multigrid method study.
His Linear system research is multidisciplinary, incorporating elements of Algorithm and Sparse matrix.
His Discretization research incorporates elements of Numerical analysis and Applied mathematics.
His Convection–diffusion equation study which covers Rate of convergence that intersects with Iterative method.
His work deals with themes such as Incomplete LU factorization and Matrix, which intersect with Preconditioner.
His most cited work include:
- High order ADI method for solving unsteady convection-diffusion problems (161 citations)
- Incomplete LU preconditioning for large scale dense complex linear systems from electromagnetic wave scattering problems (121 citations)
- Sparse inverse preconditioning of multilevel fast multipole algorithm for hybrid Integral equations in electromagnetics (113 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Mathematical analysis, Multigrid method, Mathematical optimization, Convection–diffusion equation and Linear system.
His work investigates the relationship between Mathematical analysis and topics such as Iterative method that intersect with problems in Numerical analysis and Convergence.
His biological study deals with issues like Residual, which deal with fields such as Acceleration.
Jun Zhang has researched Mathematical optimization in several fields, including Algorithm, Relaxation, Nonlinear system and Applied mathematics.
His Convection–diffusion equation research includes themes of Compact finite difference, Alternating direction implicit method and Fourth order.
Jun Zhang works mostly in the field of Linear system, limiting it down to topics relating to Sparse matrix and, in certain cases, Incomplete LU factorization, Robustness, Factorization and Sparse approximation, as a part of the same area of interest.
He most often published in these fields:
- Mathematical analysis (24.42%)
- Multigrid method (19.82%)
- Mathematical optimization (18.43%)
What were the highlights of his more recent work (between 2014-2020)?
- Centrifuge (4.15%)
- Non-negative matrix factorization (5.53%)
- Shell (2.76%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Centrifuge, Non-negative matrix factorization, Shell, Collaborative filtering and Lidar.
The study incorporates disciplines such as Machine learning, Recommender system and Artificial intelligence in addition to Non-negative matrix factorization.
His Artificial intelligence research integrates issues from Dimension and Pattern recognition.
His work on Data mining expands to the thematically related Collaborative filtering.
His work in Data mining is not limited to one particular discipline; it also encompasses Rating matrix.
His studies deal with areas such as Point cloud, Segmentation and Canopy as well as Lidar.
Between 2014 and 2020, his most popular works were:
- Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds (40 citations)
- Forest understory trees can be segmented accurately within sufficiently dense airborne laser scanning point clouds. (26 citations)
- A scalable approach for tree segmentation within small-footprint airborne LiDAR data (12 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Artificial intelligence
- Algorithm
Jun Zhang spends much of his time researching Richardson extrapolation, Multigrid method, Lidar, Point cloud and Convection–diffusion equation.
His Richardson extrapolation research focuses on Discretization and how it relates to Numerical analysis and Mathematical optimization.
His Multigrid method study combines topics from a wide range of disciplines, such as Computation and Poisson's equation.
Jun Zhang interconnects Linear system, Extrapolation and Truncation error, Applied mathematics in the investigation of issues within Poisson's equation.
His Convection–diffusion equation study necessitates a more in-depth grasp of Mathematical analysis.
His Mathematical analysis study integrates concerns from other disciplines, such as Grid and Point.
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