What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Statistics
- Algebra
Michael Griebel mainly investigates Discretization, Sparse grid, Applied mathematics, Mathematical analysis and Partial differential equation.
His Discretization study incorporates themes from Algorithm, Finite difference, Computer simulation and Effective dimension.
He studied Sparse grid and Numerical integration that intersect with Finance and Special case.
His studies deal with areas such as Logarithm, Finite element method, Norm and Solver, Mathematical optimization as well as Applied mathematics.
His Mathematical analysis research incorporates themes from Pure mathematics, Tensor product and Rate of convergence.
Michael Griebel has included themes like Myrinet, Meshfree methods, Numerical analysis and Finite difference method, Calculus in his Partial differential equation study.
His most cited work include:
- Numerical integration using sparse grids (742 citations)
- Molecular Simulation of the Influence of Chemical Cross-Links on the Shear Strength of Carbon Nanotube-Polymer Interfaces (545 citations)
- Dimension-adaptive tensor-product quadrature (440 citations)
What are the main themes of his work throughout his whole career to date?
Sparse grid, Applied mathematics, Discretization, Mathematical analysis and Grid are his primary areas of study.
His Sparse grid research integrates issues from Sparse approximation, Curse of dimensionality and Tensor product.
His Applied mathematics study combines topics from a wide range of disciplines, such as Subspace topology, Finite element method, Partition of unity, Condition number and Solver.
His work in Discretization addresses subjects such as Finite difference, which are connected to disciplines such as Finite difference method.
Michael Griebel does research in Mathematical analysis, focusing on Boundary value problem specifically.
In Grid, Michael Griebel works on issues like Multigrid method, which are connected to Parallel computing and Basis function.
He most often published in these fields:
- Sparse grid (27.09%)
- Applied mathematics (21.91%)
- Discretization (21.12%)
What were the highlights of his more recent work (between 2013-2021)?
- Applied mathematics (21.91%)
- Sparse grid (27.09%)
- Mathematical analysis (17.13%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Applied mathematics, Sparse grid, Mathematical analysis, Hilbert space and Computer simulation.
Michael Griebel has researched Applied mathematics in several fields, including Basis, Estimator, Parametric statistics and Hierarchy.
His study in Sparse grid is interdisciplinary in nature, drawing from both Discrete mathematics, Curse of dimensionality, Grid, Discretization and Product.
His study looks at the relationship between Grid and fields such as Algorithm, as well as how they intersect with chemical problems.
As a part of the same scientific family, he mostly works in the field of Discretization, focusing on Supercomputer and, on occasion, Reduction.
His study in the field of Numerical integration also crosses realms of Gauss–Jacobi quadrature, Gauss–Kronrod quadrature formula and Tanh-sinh quadrature.
Between 2013 and 2021, his most popular works were:
- Lecture Notes in Computational Science and Engineering (169 citations)
- Approximation of bi-variate functions: singular value decomposition versus sparse grids (36 citations)
- Fast Discrete Fourier Transform on Generalized Sparse Grids (31 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Statistics
- Algebra
Michael Griebel mostly deals with Sparse grid, Mathematical analysis, Discretization, Omega and Applied mathematics.
His research integrates issues of Nonlinear dimensionality reduction, Dimensionality reduction, Curse of dimensionality, Grid and Exascale computing in his study of Sparse grid.
His biological study spans a wide range of topics, including Preconditioner, Subspace topology, Mathematical optimization and Scaling.
His work in the fields of Mathematical analysis, such as Reproducing kernel Hilbert space and Function space, overlaps with other areas such as Gauss–Jacobi quadrature, Gaussian quadrature and Gauss–Kronrod quadrature formula.
The study incorporates disciplines such as Supercomputer, Scalability and Extrapolation in addition to Discretization.
His work carried out in the field of Applied mathematics brings together such families of science as Space, Fourier analysis, Tensor product and Discrete Fourier transform.
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