Sequential Experiment Design for Contour Estimation From Complex Computer Codes

By Pritam Ranjan, Derek R. Bingham, George Michailidis. 2008

312 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Reliability-based design optimization using kriging surrogates and subset simulation (2011, methodology): “Various reﬁnement techniques have been proposed in the kriging-related literature, e.g. for global optimization (Jones et al., 1998) or for probability / quantile estimation (Oakley, 2004; Bichon et al., 2008; Lee and Jung, 2008; Ranjan et al., 2008; Vazquez and Bect, 2009; Picheny et al., 2010).”
A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models (2013, methodology): “Inspired by EGO [19] and the Kriging contour estimation method [25], EGRA [5] (Efficient Global Reliability Analysis) is based on a learning function called the Expected Feasibility Function (EFF) which provides an indication of how well the true value of the performance function in a sample can be ”
Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions (2008, methodology): “Inspired by the contour estimation work in [28], this expectation can be calculated in a similar fashion as Eq.”
Sequential design of computer experiments for the estimation of a probability of failure (2010, background): “The same leading principle motivates the criteria proposed by Ranjan et al. (2008) and Bichon et al.”
Adaptive Designs of Experiments for Accurate Approximation of a Target Region (2010, unknown): “Ranjan et al. [6] proposed a modiﬁed version of the famous EGO al-gorithm (Efﬁcient Global Optimization, [7]) to sequentially explore the domain region along a contour line.”
Active learning for structural reliability: Survey, general framework and benchmark (2021, methodology): “The latter is actually an adaptation to contour estimation of the well-known expected improvement (EI) function (Ranjan et al., 2008) widely used in Bayesian optimization as first introduced in Jones et al.”