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.
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