What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Statistics
- Numerical analysis
His main research concerns Mathematical analysis, Computational fluid dynamics, Numerical analysis, Euler equations and Boundary value problem.
His Mathematical analysis study combines topics in areas such as Error detection and correction and Residual.
The Numerical analysis study combines topics in areas such as Backward Euler method, Finite element method, Estimator, System of linear equations and Nonlinear system.
His work carried out in the field of Euler equations brings together such families of science as Inviscid flow and Transonic.
His studies deal with areas such as Flow, Fourier analysis and Aerodynamics as well as Boundary value problem.
Michael B. Giles has researched Piecewise in several fields, including Stochastic differential equation, Applied mathematics and Monte Carlo method.
His most cited work include:
- Multilevel Monte Carlo Path Simulation (1093 citations)
- Viscous-inviscid analysis of transonic and low Reynolds number airfoils (779 citations)
- An Introduction to the Adjoint Approach to Design (686 citations)
What are the main themes of his work throughout his whole career to date?
Monte Carlo method, Applied mathematics, Mathematical analysis, Numerical analysis and Mathematical optimization are his primary areas of study.
His work in Monte Carlo method addresses subjects such as Estimator, which are connected to disciplines such as Expected value.
His study in Applied mathematics is interdisciplinary in nature, drawing from both Computational complexity theory, Invariant measure, Ergodic theory, Multigrid method and Discretization.
Euler equations, Boundary value problem, Adjoint equation and Order of accuracy are the subjects of his Mathematical analysis studies.
Michael B. Giles combines subjects such as Inviscid flow, Transonic and Euler's formula with his study of Euler equations.
In Numerical analysis, Michael B. Giles works on issues like Computational fluid dynamics, which are connected to Computational science.
He most often published in these fields:
- Monte Carlo method (27.90%)
- Applied mathematics (22.88%)
- Mathematical analysis (17.24%)
What were the highlights of his more recent work (between 2013-2021)?
- Monte Carlo method (27.90%)
- Applied mathematics (22.88%)
- Parallel computing (13.17%)
In recent papers he was focusing on the following fields of study:
Michael B. Giles mainly focuses on Monte Carlo method, Applied mathematics, Parallel computing, Estimator and Stochastic differential equation.
His Monte Carlo method research incorporates themes from Random variable, Brownian motion, Statistical physics, Mathematical optimization and Rate of convergence.
His Statistical physics research focuses on Dynamic Monte Carlo method and how it connects with Hybrid Monte Carlo and Monte Carlo molecular modeling.
His Applied mathematics study incorporates themes from Markov chain Monte Carlo, Invariant measure, Ergodic theory, Discretization and Numerical analysis.
His research integrates issues of Semi-implicit Euler method and Monte Carlo algorithm in his study of Numerical analysis.
His Stochastic differential equation study necessitates a more in-depth grasp of Mathematical analysis.
Between 2013 and 2021, his most popular works were:
- Multilevel Monte Carlo methods (427 citations)
- Antithetic multilevel Monte Carlo estimation for multi-dimensional SDEs without Lévy area simulation (63 citations)
- Multilevel Monte Carlo Approximation of Distribution Functions and Densities (43 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Mathematical analysis
- Algorithm
His primary areas of investigation include Monte Carlo method, Parallel computing, Applied mathematics, Estimator and Computational science.
Michael B. Giles has included themes like Stochastic differential equation, Statistical physics and Mathematical optimization in his Monte Carlo method study.
His studies in Parallel computing integrate themes in fields like Scalability, Solver and Code generation.
His Applied mathematics research includes themes of Elliptic partial differential equation, Variance reduction, Discretization, Rate of convergence and Discontinuous Galerkin method.
His research in Computational science intersects with topics in Block, Polygon mesh, Multi-core processor and General-purpose computing on graphics processing units.
As a member of one scientific family, Michael B. Giles mostly works in the field of Xeon Phi, focusing on CUDA and, on occasion, Computational fluid dynamics, Software portability, Distributed computing and Domain-specific language.
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