Design of computer experiments: space filling and beyond

By Luc Pronzato, Werner G. Müller. Statistics and computing, 2011

397 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0530ccc659b33622266980e685455e716cd541fa

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In Artificial Intelligence

In Computational Theory and Mathematics

In Management Science and Operations Research

Citations in the corpus

Least squares polynomial chaos expansion: A review of sampling strategies (2017, background): “Space-ﬁlling designs form a class of DOEs that have received extensive attention in the context of computer experiments [106, 67, 94, 62].”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, neutral): “Many modern sampling strategies optimize the way samples are taken to allow a parsimonious use of the model and to maximize efficiency given the available computational budget (Pronzato and Müller, 2012; P ´ azman and Pronzato, 2014; Sheikholeslami and Razavi, 2017; Becker et al., 2018).”
Minimax and maximin space-filling designs: some properties and methods for construction (2017, neutral): “Delaunay triangulation (Pronzato and Müller, 2012).”
Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment (2017, methodology): “[70,71] proposed the use of a stochastic model, called Kriging [72], to treat the deterministic computer response as a realization of a random function with respect to the actual system response.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “For more information about space-filling sampling, one can refer to the recent books, reviews and comparison studies (Kleijnen 2008; Pronzato and Müller 2012; Kleijnen 2015; Damblin et al. 2013; Sanchez and Wan 2015), and the interesting website https://spacefillingdesigns.”
Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, background): “Forrester and Keane [109] present a very good technical overview on the topic, and Pronzato and Müller [182] provide insights on sequential sampling for metamodel generation.”
Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence (2013, background): “The introduction of Latin hypercube sampling has been succeeded by further optimisations [33] [34] [35] [36] [37], which usually target a better space-filling character.”
Design of computer experiments: A review (2017, methodology): “Recently, Pronzato and Müller published a review article [130] aiming to detail advances beyond Kohler and Owen [96] and Chen et al. [23].”