H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Mathematics H-index 36 Citations 10,260 139 World Ranking 1354 National Ranking 79

Overview

What is he best known for?

The fields of study he is best known for:

  • Mathematical analysis
  • Statistics
  • Normal distribution

His primary areas of study are Mathematical analysis, Monte Carlo method, Collocation method, Random field and Partial differential equation. The concepts of his Mathematical analysis study are interwoven with issues in Galerkin method and Random function, Random variable. His studies in Monte Carlo method integrate themes in fields like Finite element method, Stochastic differential equation, Applied mathematics, Estimator and Mathematical optimization.

His study of Orthogonal collocation is a part of Collocation method. Raul Tempone usually deals with Orthogonal collocation and limits it to topics linked to Sparse grid and Tensor product, Collocation and Numerical analysis. In his research on the topic of Random field, Random element, Convergence of random variables, Linear elasticity and Stochastic simulation is strongly related with Multivariate random variable.

His most cited work include:

  • A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data (1173 citations)
  • A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data (704 citations)
  • GALERKIN FINITE ELEMENT APPROXIMATIONS OF STOCHASTIC ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS (644 citations)

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

Raul Tempone mostly deals with Applied mathematics, Monte Carlo method, Mathematical optimization, Algorithm and Estimator. His Applied mathematics research integrates issues from Rate of convergence and Partial differential equation. His Monte Carlo method study combines topics from a wide range of disciplines, such as Discretization, Sampling and Kalman filter.

In his study, which falls under the umbrella issue of Mathematical optimization, Ordinary differential equation is strongly linked to Numerical analysis. Raul Tempone has researched Estimator in several fields, including Random variable and Cumulative distribution function. His Sparse grid research is multidisciplinary, relying on both Orthogonal collocation, Collocation method, Sparse approximation and Collocation.

He most often published in these fields:

  • Applied mathematics (42.30%)
  • Monte Carlo method (44.59%)
  • Mathematical optimization (21.64%)

What were the highlights of his more recent work (between 2018-2021)?

  • Monte Carlo method (44.59%)
  • Algorithm (21.64%)
  • Applied mathematics (42.30%)

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

His primary areas of investigation include Monte Carlo method, Algorithm, Applied mathematics, Estimator and Uncertainty quantification. His research on Monte Carlo method focuses in particular on Importance sampling. His biological study focuses on Sparse grid.

His study explores the link between Stochastic optimization and topics such as Differentiable function that cross with problems in Mathematical optimization. His Discretization research is multidisciplinary, incorporating elements of Numerical analysis and Variance reduction. The various areas that Raul Tempone examines in his Mathematical analysis study include Stochastic modelling and Divergence.

Between 2018 and 2021, his most popular works were:

  • IGA-based multi-index stochastic collocation for random PDEs on arbitrary domains (8 citations)
  • IGA-based multi-index stochastic collocation for random PDEs on arbitrary domains (8 citations)
  • Pricing American options by exercise rate optimization (6 citations)

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

  • Statistics
  • Mathematical analysis
  • Algorithm

Raul Tempone mainly investigates Algorithm, Monte Carlo method, Applied mathematics, Uncertainty quantification and Collocation. His research in Algorithm intersects with topics in State space, Hierarchy and Gibbs sampling. His Monte Carlo method study incorporates themes from Stochastic optimization, Mathematical optimization, Laplace's method and Gaussian noise.

His work carried out in the field of Applied mathematics brings together such families of science as Hurst exponent, Valuation of options, Adaptive algorithm, Quasi-Monte Carlo method and Sample. His Uncertainty quantification research includes themes of Curve fitting, Identifiability, Inverse problem and Artificial intelligence. His Collocation research includes elements of Isogeometric analysis and Tensor.

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.

Top Publications

A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

Ivo Babus caron;ka;Fabio Nobile;Rau´l Tempone.
SIAM Journal on Numerical Analysis (2007)

1607 Citations

A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

F. Nobile;R. Tempone;C. G. Webster.
SIAM Journal on Numerical Analysis (2008)

1144 Citations

GALERKIN FINITE ELEMENT APPROXIMATIONS OF STOCHASTIC ELLIPTIC PARTIAL DIFFERENTIAL EQUATIONS

Ivo Babuska;Raúl Tempone;Georgios E. Zouraris.
SIAM Journal on Numerical Analysis (2004)

1039 Citations

An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

F. Nobile;R. Tempone;C. G. Webster.
SIAM Journal on Numerical Analysis (2008)

644 Citations

Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation

Ivo Babuška;Raúl Tempone;Georgios E. Zouraris.
Computer Methods in Applied Mechanics and Engineering (2005)

392 Citations

A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data

Ivo Babuška;Fabio Nobile;Raúl Tempone.
Siam Review (2010)

380 Citations

Stochastic Spectral Galerkin and Collocation Methods for PDEs with Random Coefficients: A Numerical Comparison

Joakim Bäck;Fabio Nobile;Lorenzo Tamellini;Raul Tempone.
Spectral and high order methods for partial differential equations : selected papers from the ICOSAHOM ’09 Conference, June 22-26, Trondheim, Norway (2011)

218 Citations

ON THE OPTIMAL POLYNOMIAL APPROXIMATION OF STOCHASTIC PDES BY GALERKIN AND COLLOCATION METHODS

Joakim Beck;Raul Tempone;Fabio Nobile;Fabio Nobile;Lorenzo Tamellini.
Mathematical Models and Methods in Applied Sciences (2012)

157 Citations

Multi-index Monte Carlo: when sparsity meets sampling

Abdul-Lateef Haji-Ali;Fabio Nobile;Raúl Tempone.
Numerische Mathematik (2016)

130 Citations

Analysis and implementation issues for the numerical approximation of parabolic equations with random coefficients

Fabio Nobile;Raul Tempone;Raul Tempone.
International Journal for Numerical Methods in Engineering (2009)

118 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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