What is he best known for?
The fields of study he is best known for:
- Quantum mechanics
- Mathematical analysis
- Algebra
Robert D. Skeel mainly focuses on Molecular dynamics, Discretization, Mathematical analysis, Computational science and Impulse.
His Molecular dynamics research integrates issues from Tree, Load balancing, Statistical physics and Parallel computing.
The concepts of his Mathematical analysis study are interwoven with issues in Verlet integration and Dynamical systems theory.
The Dynamical systems theory study combines topics in areas such as Numerical integration, Applied mathematics, Algorithm and Differential equation.
His Impulse research is multidisciplinary, incorporating elements of Stochastic process, Langevin equation, Computational chemistry, Newtonian fluid and Autocorrelation.
He integrates several fields in his works, including Molecular graphics, Multipole expansion, Object-oriented programming, Source code, Scripting language and Software.
His most cited work include:
- Scalable molecular dynamics with NAMD (11850 citations)
- NAMD2: Greater Scalability for Parallel Molecular Dynamics (1962 citations)
- Langevin stabilization of molecular dynamics (532 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Applied mathematics, Mathematical analysis, Statistical physics, Molecular dynamics and Symplectic integrator.
His work deals with themes such as Verlet integration, Mathematical optimization, Ordinary differential equation and Hybrid Monte Carlo, which intersect with Applied mathematics.
Robert D. Skeel combines subjects such as Path, Computation and Markov chain with his study of Statistical physics.
His Molecular dynamics research focuses on subjects like Classical mechanics, which are linked to Brownian dynamics.
His research in the fields of Leapfrog integration overlaps with other disciplines such as Symplectic geometry, Hamiltonian system and Numerical integration.
His research in Discretization tackles topics such as Nonlinear system which are related to areas like Equations of motion, Impulse and Algorithm.
He most often published in these fields:
- Applied mathematics (31.25%)
- Mathematical analysis (22.32%)
- Statistical physics (18.75%)
What were the highlights of his more recent work (between 2008-2019)?
- Statistical physics (18.75%)
- Hybrid Monte Carlo (5.36%)
- Applied mathematics (31.25%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Statistical physics, Hybrid Monte Carlo, Applied mathematics, Markov chain Monte Carlo and Monte Carlo method.
His biological study spans a wide range of topics, including Transition rate matrix, Path, Computation and Markov chain.
His Applied mathematics research integrates issues from Function, Potential energy, Classical mechanics and Holonomic.
His Markov chain Monte Carlo study incorporates themes from Numerical analysis and Autocorrelation.
Integral equation is a primary field of his research addressed under Mathematical analysis.
The various areas that Robert D. Skeel examines in his Mathematical analysis study include Finite element method and Nonlinear system.
Between 2008 and 2019, his most popular works were:
- Bayesian Sampling Using Stochastic Gradient Thermostats (154 citations)
- Compressible generalized hybrid Monte Carlo (36 citations)
- Multilevel summation method for electrostatic force evaluation. (36 citations)
In his most recent research, the most cited papers focused on:
- Quantum mechanics
- Mathematical analysis
- Algebra
Robert D. Skeel mostly deals with Applied mathematics, Hybrid Monte Carlo, Monte Carlo method, Statistical physics and Symplectic geometry.
His study in Applied mathematics is interdisciplinary in nature, drawing from both Probability distribution, Phase space, Markov chain Monte Carlo, Stochastic process and Configuration space.
His Hybrid Monte Carlo research includes elements of Discretization, Sampling, Numerical analysis and Mathematical optimization.
The Statistical physics study combines topics in areas such as Monte Carlo method in statistical physics, Path, Point and Invariant.
His research on Symplectic geometry concerns the broader Mathematical analysis.
His Energy conservation study spans across into fields like Symplectic integrator and Hamiltonian.
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