What is he best known for?
The fields of study he is best known for:
- Statistics
- Normal distribution
- Artificial intelligence
Jack P. C. Kleijnen spends much of his time researching Kriging, Latin hypercube sampling, Mathematical optimization, Metamodeling and Design of experiments.
Jack P. C. Kleijnen has researched Kriging in several fields, including Deterministic simulation, Linear regression, Algorithm, Global optimization and Interpolation.
The concepts of his Latin hypercube sampling study are interwoven with issues in Discrete event simulation, Regression analysis, Polynomial regression and Statistic.
Jack P. C. Kleijnen combines subjects such as Estimator, Fractional factorial design and Response surface methodology with his study of Mathematical optimization.
His studies in Metamodeling integrate themes in fields like Regression and Flexibility.
His Design of experiments research is multidisciplinary, relying on both Verification and validation, Statistical theory, Polynomial and Management science.
His most cited work include:
- Kriging metamodeling in simulation : A review (699 citations)
- Statistical tools for simulation practitioners (547 citations)
- State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments (538 citations)
What are the main themes of his work throughout his whole career to date?
Jack P. C. Kleijnen mainly investigates Mathematical optimization, Kriging, Statistics, Regression analysis and Monte Carlo method.
His Mathematical optimization research incorporates themes from Design of experiments, Polynomial, Taguchi methods and Response surface methodology.
His Design of experiments study combines topics in areas such as Algorithm and Statistical theory.
His Kriging study integrates concerns from other disciplines, such as Deterministic simulation, Latin hypercube sampling, Global optimization and Metamodeling.
The Metamodeling study combines topics in areas such as Function and Data mining.
His Regression analysis study also includes fields such as
- Linear regression that connect with fields like Generalized least squares,
- Sensitivity and related Uncertainty analysis.
He most often published in these fields:
- Mathematical optimization (23.37%)
- Kriging (23.17%)
- Statistics (22.15%)
What were the highlights of his more recent work (between 2011-2021)?
- Kriging (23.17%)
- Gaussian process (12.80%)
- Mathematical optimization (23.37%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Kriging, Gaussian process, Mathematical optimization, Monte Carlo method and Metamodeling.
His Kriging research integrates issues from Deterministic simulation and Algorithm, Global optimization.
His work deals with themes such as Computer experiment and Interpolation, which intersect with Algorithm.
His research in Mathematical optimization intersects with topics in Decision rule, Resampling, Bootstrapping and Response surface methodology.
His study focuses on the intersection of Response surface methodology and fields such as Regression analysis with connections in the field of Cross-validation.
Jack P. C. Kleijnen interconnects Design of experiments and Regression in the investigation of issues within Metamodeling.
Between 2011 and 2021, his most popular works were:
- Regression and Kriging metamodels with their experimental designs in simulation: A review (129 citations)
- Expected improvement in efficient global optimization through bootstrapped kriging (76 citations)
- Robust Optimization in Simulation: Taguchi and Krige Combined (58 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Normal distribution
- Artificial intelligence
The scientist’s investigation covers issues in Kriging, Gaussian process, Mathematical optimization, Metamodeling and Design of experiments.
Kriging is a subfield of Statistics that Jack P. C. Kleijnen tackles.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Variogram and Robust control.
Many of his studies on Metamodeling apply to Regression as well.
His Design of experiments research includes themes of Simple, Delta method, Constraint, Toy problem and Statistic.
His research in Robustness focuses on subjects like Response surface methodology, which are connected to Latin hypercube sampling.
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