Qualitative and Quantitative Sequential Sampling

Rai, Rahul; Campbell, Matthew I.

doi:10.1115/detc2006-99178

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…In the works of Kleijnen and Van Beers, an improved kriging variance estimate [94] is used and that might be a reason for better results. --2006 -Rai [159] introduced the qualitative and quantitative sequential sampling technique. The method combines information from multiple sources (including computer models and the designer's qualitative intuitions) through a criterion called ''confidence function.''…”

Section: Sample Of Papers Published Between 1998 and 2010mentioning

confidence: 99%

Surrogate modeling: tricks that endured the test of time and some recent developments

Viana

Gogu

Goel³

2021

Struct Multidisc Optim

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Tasks such as analysis, design optimization, and uncertainty quantification can be computationally expensive. Surrogate modeling is often the tool of choice for reducing the burden associated with such data-intensive tasks. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes various advanced, yet often straightforward, statistical tools that help. We focus on four techniques with increasing popularity in the surrogate modeling community: (i) variable screening and dimensionality reduction in both the input and the output spaces, (ii) data sampling techniques or design of experiments, (iii) simultaneous use of multiple surrogates, and (iv) sequential sampling. We close the paper with some suggestions for future research.

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Section: Sample Of Papers Published Between 1998 and 2010mentioning

confidence: 99%

Surrogate modeling: tricks that endured the test of time and some recent developments

Viana

Gogu

Goel³

2021

Struct Multidisc Optim

View full text Add to dashboard Cite

show abstract

“…They determined this region as the location with higher variations in the surrogate. Rai 28 proposed the Qualitative and Quantitative Sequential Sampling technique in which the concept of a confidence function was used as the criterion for selecting infill points. In this case, the confidence function was a function of the designer's qualitative choices.…”

Section: Sequential Sampling Reviewmentioning

confidence: 99%

Surrogate-Based Design Optimization with Adaptive Sequential Sampling

Mehmani¹,

Zhang²,

Chowdhury³

et al. 2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

11
0
7
0

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In spite of the recent developments in surrogate modeling techniques, the low fidelity of these models often limits their use in practical engineering design optimization. When surrogate models are used to represent the behavior of a complex system, it is challenging to simultaneously obtain high accuracy over the entire design space. When such surrogates are used for optimization, it becomes challening to find the optimum/optima with certainty. Sequential sampling methods offer a powerful solution to this challenge by providing the surrogate with reasonable accuracy where and when needed. When surrogate-based design optimization (SBDO) is performed using sequential sampling, the typical SBDO process is repeated multiple times, where each time the surrogate is improved by addition of new sample points. This paper presents a new adaptive approach to add infill points during SBDO, called Adaptive Sequential Sampling (ASS). In this approach, both local exploitation and global exploration aspects are considered for updating the surrogate during optimization, where multiple iterations of the SBDO process is performed to increase the quality of the optimal solution. This approach adaptively improves the accuracy of the surrogate in the region of the current global optimum as well as in the regions of higher relative errors. Based on the initial sample points and the fitted surrogate, the ASS method adds infill points at each iteration in the locations of: (i) the current optimum found based on the fitted surrogate; and (ii) the points generated using cross-over between sample points that have relatively higher cross-validation errors. The Nelder and Mead Simplex method is adopted as the optimization algorithm. The effectiveness of the proposed method is illustrated using a series of standard numerical test problems.

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“…Recent developments in sequential sampling are exemplified by refs. [104]- [107]. Rai [104] introduced the qualitative and quantitative sequential sampling technique.…”

Section: 1mentioning
confidence: 99%

“…[104]- [107]. Rai [104] introduced the qualitative and quantitative sequential sampling technique. The method combines information from multiple sources (including computer models and the designer's qualitative intuitions) through a criterion called "confidence function."…”

Section: 1mentioning
confidence: 99%

Making the Most Out of Surrogate Models: Tricks of the Trade
Viana
¹
,
Gogu
²
,
Haftka
³

2010
Volume 1: 36th Design Automation Conference, Parts a and B
51
0
25
0
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Design analysis and optimization based on high-fidelity computer experiments is commonly expensive. Surrogate modeling is often the tool of choice for reducing the computational burden. However, even after years of intensive research, surrogate modeling still involves a struggle to achieve maximum accuracy within limited resources. This work summarizes advanced and yet simple statistical tools that help. We focus on four techniques with increasing popularity in the design automation community: (i) screening and variable reduction in both the input and the output spaces, (ii) simultaneous use of multiple surrogates, (iii) sequential sampling and optimization, and (iv) conservative estimators.

show abstract

Qualitative and Quantitative Sequential Sampling

Cited by 9 publications

References 12 publications

Surrogate modeling: tricks that endured the test of time and some recent developments

Surrogate modeling: tricks that endured the test of time and some recent developments

Surrogate-Based Design Optimization with Adaptive Sequential Sampling

Making the Most Out of Surrogate Models: Tricks of the Trade

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