D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Mathematics D-index 32 Citations 4,716 132 World Ranking 1772 National Ranking 110

Overview

What is he best known for?

The fields of study he is best known for:

  • Quantum mechanics
  • Mathematical analysis
  • Statistics

Tony Lelièvre spends much of his time researching Mathematical analysis, Statistical physics, Applied mathematics, Molecular dynamics and Conditional expectation. As part of the same scientific family, Tony Lelièvre usually focuses on Mathematical analysis, concentrating on Shear flow and intersecting with Bounded function, Probabilistic logic, Dirichlet problem and Dirichlet boundary condition. His work on Langevin equation as part of his general Statistical physics study is frequently connected to Computational magnetohydrodynamics, thereby bridging the divide between different branches of science.

The various areas that he examines in his Applied mathematics study include Discretization and Thermodynamic integration. His Reaction coordinate research incorporates themes from Entropy, Potential energy and Metastability. Tony Lelièvre interconnects Flow, Coupling and Nonlinear system in the investigation of issues within Stochastic differential equation.

His most cited work include:

  • Free Energy Computations: A Mathematical Perspective (230 citations)
  • The adaptive biasing force method: everything you always wanted to know but were afraid to ask. (177 citations)
  • Mathematical Methods for the Magnetohydrodynamics of Liquid Metals (155 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of study are Statistical physics, Applied mathematics, Mathematical analysis, Molecular dynamics and Stochastic differential equation. His work on Langevin dynamics as part of general Statistical physics study is frequently linked to Replica, bridging the gap between disciplines. His studies in Applied mathematics integrate themes in fields like Stochastic process, Mathematical optimization, Nonlinear system, Discretization and Numerical analysis.

His work in the fields of Mathematical analysis, such as Differential equation, Boundary value problem, Fokker–Planck equation and Uniqueness, intersects with other areas such as Rate of convergence. His study focuses on the intersection of Molecular dynamics and fields such as Energy with connections in the field of Computation. The various areas that Tony Lelièvre examines in his Stochastic differential equation study include Stochastic partial differential equation and Variance reduction.

He most often published in these fields:

  • Statistical physics (30.08%)
  • Applied mathematics (28.05%)
  • Mathematical analysis (26.02%)

What were the highlights of his more recent work (between 2016-2021)?

  • Statistical physics (30.08%)
  • Applied mathematics (28.05%)
  • Molecular dynamics (19.92%)

In recent papers he was focusing on the following fields of study:

Statistical physics, Applied mathematics, Molecular dynamics, Langevin dynamics and Stochastic differential equation are his primary areas of study. In his study, State space is strongly linked to Markov process, which falls under the umbrella field of Statistical physics. His Applied mathematics research is multidisciplinary, relying on both Reaction coordinate, Leverage, Concentration of measure, Probability measure and Hybrid Monte Carlo.

His study on Molecular dynamics also encompasses disciplines like

  • Machine learning together with Collective variables,
  • Computation which connect with Field,
  • Sampling together with Energy. His research in Langevin dynamics intersects with topics in Stochastic process, Distribution and Metastability. His work is dedicated to discovering how Stochastic differential equation, Monte Carlo method are connected with Tensor product and Propagator and other disciplines.

Between 2016 and 2021, his most popular works were:

  • Smoothed Biasing Forces Yield Unbiased Free Energies with the Extended-System Adaptive Biasing Force Method. (34 citations)
  • Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. (17 citations)
  • Machine Learning Force Fields and Coarse-Grained Variables in Molecular Dynamics: Application to Materials and Biological Systems. (17 citations)

In his most recent research, the most cited papers focused on:

  • Quantum mechanics
  • Statistics
  • Mathematical analysis

Tony Lelièvre mostly deals with Langevin dynamics, Statistical physics, Stochastic process, Applied mathematics and Molecular dynamics. His study in Statistical physics is interdisciplinary in nature, drawing from both State space, Markov process and Markov chain, Stationary distribution. His Stochastic process study integrates concerns from other disciplines, such as Conditional expectation and Nonlinear system.

His Applied mathematics research is multidisciplinary, incorporating perspectives in Reaction coordinate, Series, Limit and Probability measure. His work deals with themes such as Complex system, Artificial intelligence, Machine learning and Computation, which intersect with Molecular dynamics. The Mathematical analysis study combines topics in areas such as Brownian dynamics and Monte Carlo method.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Free Energy Computations: A Mathematical Perspective

Tony Lelièvre;Mathias Rousset;Gabriel Stoltz.
(2010)

428 Citations

Mathematical Methods for the Magnetohydrodynamics of Liquid Metals

Jean-Frédéric Gerbeau;Claude Le Bris;Tony Lelièvre.
(2006)

291 Citations

The adaptive biasing force method: everything you always wanted to know but were afraid to ask.

Jeffrey Comer;James C. Gumbart;Jérôme Hénin;Tony Lelièvre.
Journal of Physical Chemistry B (2015)

257 Citations

Existence of solution for a micro–macro model of polymeric fluid: the FENE model

Benjamin Jourdain;Tony Lelièvre;Claude Le Bris.
Journal of Functional Analysis (2004)

149 Citations

Reduced basis techniques for stochastic problems

S. Boyaval;C. Le Bris;T. Lelievre;Yvon Maday.
Archives of Computational Methods in Engineering (2010)

147 Citations

Computation of free energy profiles with parallel adaptive dynamics.

Tony Lelièvre;Mathias Rousset;Gabriel Stoltz.
Journal of Chemical Physics (2007)

119 Citations

Results and Questions on a Nonlinear Approximation Approach for Solving High-dimensional Partial Differential Equations

C. Le Bris;T. Lelièvre;Y. Maday.
Constructive Approximation (2009)

118 Citations

A mathematical formalization of the parallel replica dynamics

Claude Le Bris;Tony Lelièvre;Mitchell B Luskin;Danny Perez.
Monte Carlo Methods and Applications (2012)

106 Citations

Long-Time Asymptotics of a Multiscale Model for Polymeric Fluid Flows

Benjamin Jourdain;Claude Le Bris;Tony Lelièvre;Tony Lelièvre;Félix Otto.
Archive for Rational Mechanics and Analysis (2006)

103 Citations

Partial differential equations and stochastic methods in molecular dynamics

Tony Lelièvre;Gabriel Stoltz.
Acta Numerica (2016)

102 Citations

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