What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Quantum mechanics
- Statistics
His scientific interests lie mostly in Mathematical analysis, Discrete mathematics, Quasi-Monte Carlo method, Combinatorics and Lattice.
His Mathematical analysis study frequently draws parallels with other fields, such as Finite element method.
The Discrete mathematics study combines topics in areas such as Dimension, Bounded function, Uniform boundedness, Polynomial interpolation and Sobolev space.
The various areas that he examines in his Quasi-Monte Carlo method study include Quantum Monte Carlo, Monte Carlo integration and Dynamic Monte Carlo method.
His study in the field of Degree and Unit sphere is also linked to topics like Function representation.
His Lattice research is multidisciplinary, incorporating elements of Periodic function, Unit cube, Numerical analysis and Applied mathematics.
His most cited work include:
- Lattice Methods for Multiple Integration (655 citations)
- When Are Quasi-Monte Carlo Algorithms Efficient for High Dimensional Integrals? (551 citations)
- High-dimensional integration: The quasi-Monte Carlo way (400 citations)
What are the main themes of his work throughout his whole career to date?
Ian H. Sloan mainly focuses on Mathematical analysis, Applied mathematics, Combinatorics, Discrete mathematics and Integral equation.
In his study, Discretization is inextricably linked to Finite element method, which falls within the broad field of Mathematical analysis.
His Applied mathematics research also works with subjects such as
- Quasi-Monte Carlo method together with Monte Carlo integration,
- Random field that connect with fields like Circulant matrix and Isotropy.
His research integrates issues of Numerical integration and Upper and lower bounds in his study of Combinatorics.
The concepts of his Discrete mathematics study are interwoven with issues in Dimension, Lattice, Bounded function, Sobolev space and Function.
His study in Integral equation is interdisciplinary in nature, drawing from both Singularity and Logarithm.
He most often published in these fields:
- Mathematical analysis (37.42%)
- Applied mathematics (18.40%)
- Combinatorics (16.56%)
What were the highlights of his more recent work (between 2015-2021)?
- Applied mathematics (18.40%)
- Random field (6.44%)
- Quasi-Monte Carlo method (11.96%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Applied mathematics, Random field, Quasi-Monte Carlo method, Mathematical analysis and Sobolev space.
His research in Applied mathematics intersects with topics in Uncertainty quantification, Covariance matrix, Function and Elliptic partial differential equation.
His Quasi-Monte Carlo method research is multidisciplinary, incorporating perspectives in Monte Carlo integration, Dynamic Monte Carlo method and Finite element method.
Ian H. Sloan incorporates Mathematical analysis and Regularization perspectives on support vector machines in his studies.
His Sobolev space study incorporates themes from Smoothing, Smoothness, Discrete mathematics and Series.
His work on Existential quantification and Unit cube as part of general Discrete mathematics study is frequently connected to High dimensional, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Between 2015 and 2021, his most popular works were:
- Multilevel Quasi-Monte Carlo methods for lognormal diffusion problems (46 citations)
- Analysis of Circulant Embedding Methods for Sampling Stationary Random Fields (23 citations)
- Random Point Sets on the Sphere—Hole Radii, Covering, and Separation (22 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Quantum mechanics
- Statistics
His primary areas of study are Applied mathematics, Random field, Mathematical analysis, Quasi-Monte Carlo method and Unit sphere.
Ian H. Sloan studied Applied mathematics and Covariance that intersect with Uncertainty quantification.
His Random field research incorporates elements of Circulant matrix, Numerical analysis, Eigenvalues and eigenvectors and Gaussian.
He has included themes like Isotropy and Radius in his Mathematical analysis study.
His Quasi-Monte Carlo method study integrates concerns from other disciplines, such as Discretization and Numerical integration.
His Unit sphere study combines topics from a wide range of disciplines, such as Pareto principle, Bounded function and Sparse regularization.
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