Sequential Laplacian regularized V-optimal design of experiments for response surface modeling of expensive tests: An application in wind tunnel testing

Alaeddini, Adel; Craft, Edward; Meka, Rajitha; Martinez, Stanford

doi:10.1080/24725854.2018.1508928

Cited by 11 publications

(12 citation statements)

References 60 publications

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“…An appropriate choice of similarity matrix should contain symmetric weights S ij (S ij = S ji ) which imposes a heavy penalty if neighboring points x i and x j are mapped far apart, i.e. nearest neighbour (Alaeddini, Craft et al 2019). In Section III-B we propose a novel formulation for constructing graph Laplacian which considers similarity of both predictor and response variable spaces.…”

Section: A Laplacian Regularized Active Learning (Lr-al)mentioning

confidence: 99%

“…SLRV selects the next evaluation point that minimizes the Laplacian regularized V-optimality criterion based on the locally weighted regression (LOESS) using both evaluated and unevaluated points [2]:…”

Section: ) Sequential Laplacian Regularized V-optimal (Slrv)mentioning

confidence: 99%

“…I N many real-world problems, we encounter situations involving estimation of expensive noisy blackbox functions. These problems typically require a large number of evaluations that could take hours or days for evaluating one single point [1], [2]. Traditional response surface methods [3], which are based on simple parametric models, may not properly approximate these expensive black-box functions.…”

Section: Introductionmentioning

confidence: 99%

“…Later, they show how the Gaussian random fields and harmonic energy minimizing framework can be viewed as GP with covariance matrix derived from graph Laplacian [64]. Alaeddini et al [2] propose an active learning methodology based on sequential Laplacian regularized V-optimal design of experiments for efficient estimation of the black box functions.…”

Section: Introductionmentioning

confidence: 99%

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An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process Regression

et al. 2020

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Estimation of black-box functions often requires evaluating an extensive number of expensive noisy points. Learning algorithms can actively compare the similarity between the evaluated and unevaluated points to determine the most informative subsequent points for efficient estimation of expensive functions in a sequential procedure. In this paper, we propose an active learning methodology based on the integration of Laplacian regularization and active learning-Cohn (ALC) measure for identification of the most informative points for efficient estimation of noisy black-box functions using Gaussian processes. We propose two simple greedy search algorithms for sequential optimization of the tuning parameters and determination of subsequent points based on the information from the previously evaluated points. We also enhance the graph Laplacian with the information of both the predictor and response variables to capture the similarity between the points more effectively. The proposed methodology is particularly suited for problems involving estimation of expensive black-box functions with a high level of noise and plenty of unevaluated points. Using a case study for analysis of the kinematics of pitching in baseball as well as simulation experiments, we demonstrate the performance of the proposed methodology against existing methods in the literature in terms of estimation error.

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Section: A Laplacian Regularized Active Learning (Lr-al)mentioning

confidence: 99%

Section: ) Sequential Laplacian Regularized V-optimal (Slrv)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process Regression

et al. 2020

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“…and Chen et al (2010),Alaeddini et al (2019) choose the V-optimal design criterion for LapRLS model and work on the corresponding objective function.In addition, Yao et al…”

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confidence: 99%

On active learning methods for manifold data

Runger

2020

TEST

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Active learning is a major area of interest within the field of machine learning, especially when the labeled instances are very difficult, time-consuming or expensive to obtain. In this paper, we review various active learning methods for manifold data, where the intrinsic manifold structure of data is also incorporated into the active learning query strategies. In addition, we present a new manifold-based active learning algorithm for Gaussian process classification. This new method uses a data-dependent kernel derived from a semi-supervised model that considers both labeled and unlabeled data. The method performs a regularization on the smoothness of the fitted function with respect to both the ambient space and the manifold where the data lie. The regularization parameter is treated as an additional kernel (covariance) parameter and estimated from the data, permitting adaptation of the kernel to the given dataset manifold geometry. Comparisons with other AL methods for manifold data show faster learning performance in our empirical experiments. MATLAB code that reproduces all examples is provided as supplementary materials.

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Design of Experiments and machine learning for product innovation: A systematic literature review

Arboretti

Ceccato

Pegoraro

et al. 2021

Quality & Reliability Eng

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The recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques. This article aims to provide a systematic overview of the literature on the joint application of DOE and ML in product innovation (PI) settings. To this end, a systematic literature review (SLR) of two major scientific databases is conducted, retrieving 388 papers, of which 86 are selected for careful analysis. The results of this review delineate the state of the art and identify the main trends in terms of experimental designs and ML algorithms selected for joint application on PI. The gaps, open problems, and research opportunities are identified, and directions for future research are provided.

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Sequential Laplacian regularized V-optimal design of experiments for response surface modeling of expensive tests: An application in wind tunnel testing

Cited by 11 publications

References 60 publications

An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process Regression

An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process Regression

On active learning methods for manifold data

Design of Experiments and machine learning for product innovation: A systematic literature review

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