What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Mathematical analysis
His scientific interests lie mostly in Polynomial chaos, Mathematical optimization, Algorithm, Kriging and Applied mathematics.
His research integrates issues of Random variable, Uncertainty quantification, Adaptive algorithm, Finite element method and Random field in his study of Polynomial chaos.
Bruno Sudret works mostly in the field of Mathematical optimization, limiting it down to concerns involving Reliability and, occasionally, Limit state design.
As a member of one scientific family, Bruno Sudret mostly works in the field of Algorithm, focusing on Least-angle regression and, on occasion, Environmental exposure.
His Kriging research includes elements of Computational model, Surrogate model and Metamodeling.
Bruno Sudret has researched Applied mathematics in several fields, including Computation and Sensitivity.
His most cited work include:
- Global sensitivity analysis using polynomial chaos expansions (1268 citations)
- Adaptive sparse polynomial chaos expansion based on least angle regression (659 citations)
- An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis (462 citations)
What are the main themes of his work throughout his whole career to date?
Polynomial chaos, Uncertainty quantification, Mathematical optimization, Applied mathematics and Algorithm are his primary areas of study.
Bruno Sudret combines subjects such as Sobol sequence, Sensitivity, Least-angle regression and Random variable with his study of Polynomial chaos.
His studies deal with areas such as Polynomial, Econometrics, Computational model and Benchmark as well as Uncertainty quantification.
Bruno Sudret interconnects Finite element method, Kriging, Metamodeling, Reliability and Monte Carlo method in the investigation of issues within Mathematical optimization.
His work deals with themes such as Sparse polynomial, Statistics and Rank, which intersect with Applied mathematics.
His work in Algorithm addresses issues such as Bayesian inference, which are connected to fields such as Markov chain Monte Carlo and Calibration.
He most often published in these fields:
- Polynomial chaos (31.14%)
- Uncertainty quantification (27.84%)
- Mathematical optimization (24.85%)
What were the highlights of his more recent work (between 2018-2021)?
- Uncertainty quantification (27.84%)
- Algorithm (18.56%)
- Polynomial chaos (31.14%)
In recent papers he was focusing on the following fields of study:
Bruno Sudret mostly deals with Uncertainty quantification, Algorithm, Polynomial chaos, Sensitivity and Reliability.
His Uncertainty quantification research includes elements of Random variable, Risk analysis, Applied mathematics, Computational model and Polynomial.
His research in Algorithm intersects with topics in Embedding, Benchmark, Curse of dimensionality and Bayesian inference.
The concepts of his Polynomial chaos study are interwoven with issues in Dimension, Markov chain Monte Carlo, Material properties, Least-angle regression and Probabilistic logic.
A large part of his Sensitivity studies is devoted to Sobol sequence.
His research in Reliability tackles topics such as Kriging which are related to areas like Mathematical optimization, Surrogate model, Support vector machine and Artificial intelligence.
Between 2018 and 2021, his most popular works were:
- Euclid preparation: II. The EuclidEmulator – a tool to compute the cosmology dependence of the nonlinear matter power spectrum (72 citations)
- A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas (34 citations)
- Surrogate-assisted reliability-based design optimization: a survey and a unified modular framework (31 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Mathematical analysis
His primary areas of study are Polynomial chaos, Uncertainty quantification, Algorithm, Sobol sequence and Kriging.
His Polynomial chaos study which covers Material properties that intersects with Calibration and Finite element method.
His Uncertainty quantification study combines topics from a wide range of disciplines, such as Markov chain Monte Carlo, Spectral density, Mathematical optimization, Statistical model and Regression analysis.
His work on Computational model as part of general Algorithm study is frequently linked to Context, therefore connecting diverse disciplines of science.
Sensitivity and Monte Carlo method are all intrinsically tied to his study in Sobol sequence.
His research integrates issues of Reliability and Surrogate model in his study of Kriging.
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