A Clustering-Based Surrogate Model Updating Approach to Simulation-Based Engineering Design

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Tiefu Shao ◽  
Sundar Krishnamurty
Keyword(s):  
Design Optimization ◽  
Engineering Design ◽  
Surrogate Model ◽  
Model Updating ◽  
Surrogate Models ◽  
Illustrative Case ◽  
Design Decision ◽  
Optimal Point ◽  
Model Based ◽  
Simulation Based

This paper addresses the critical issue of effectiveness and efficiency in simulation-based optimization using surrogate models as predictive models in engineering design. Specifically, it presents a novel clustering-based multilocation search (CMLS) procedure to iteratively improve the fidelity and efficacy of Kriging models in the context of design decisions. The application of this approach will overcome the potential drawback in surrogate-model-based design optimization, namely, the use of surrogate models may result in suboptimal solutions due to the possible smoothing out of the global optimal point if the sampling scheme fails to capture the critical points of interest with enough fidelity or clarity. The paper details how the problem of smoothing out the best (SOB) can remain unsolved in multimodal systems, even if a sequential model updating strategy has been employed, and lead to erroneous outcomes. Alternatively, to overcome the problem of SOB defect, this paper presents the CMLS method that uses a novel clustering-based methodical procedure to screen out distinct potential optimal points for subsequent model validation and updating from a design decision perspective. It is embedded within a genetic algorithm setup to capture the buried, transient, yet inherent data pattern in the design evolution based on the principles of data mining, which are then used to improve the overall performance and effectiveness of surrogate-model-based design optimization. Four illustrative case studies, including a 21bar truss problem, are detailed to demonstrate the application of the CMLS methodology and the results are discussed.

A Hybrid Method for Surrogate Model Updating in Engineering Design Optimization

2007 ◽  
Author(s):  
Tiefu Shao ◽  
Sundar Krishnamurthy
Keyword(s):  
Design Optimization ◽  
Surrogate Model ◽  
Design Space ◽  
Model Updating ◽  
Salient Feature ◽  
Critical Issue ◽  
Surrogate Models ◽  
Point Method ◽  
Optimal Point ◽  
Global Optimal

This paper addresses the critical issue of effectiveness, efficiency, and reliability in simulation-based design optimization under surrogate model uncertainty. Specifically, it presents a novel method to build surrogate models iteratively with sufficient fidelity for accurately capturing global optimal design solutions at a minimal cost. The salient feature of the proposed method lies in its unique preference of focusing necessarily high fidelity at potential global optimal regions of surrogate models. The proposed method is the synergic integration of the multiple preference point method, which updates surrogate model at current local optimal points predicted with data-mining techniques in genetic algorithm setup, and the maximum variance point method, which updates surrogate model at the point associated with the maximum prediction variance. Through illustrative comparison studies on thirty different optimization scenarios derived from 15 different test functions, the proposed method demonstrates the tangible reliability advancement. The experimental results indicate that the proposed method can be a reliable updating method in surrogate-model-based design optimization for efficiently locating the global optimal point/points in various kinds of optimization scenarios featured by single/multiple global optimal point/points that may exist at the corners of design space, inside design space, or on the boundaries of design space.

Surrogate Model Updating Using Clustering in a Genetic Algorithm Setup

2006 ◽  
Author(s):  
Tiefu Shao ◽  
Sundar Krishnamurty
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Model Building ◽  
Model Updating ◽  
Cost Effective ◽  
Critical Issue ◽  
Surrogate Models ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Updating Procedure

This paper addresses the critical issue of fidelity in simulation-based design optimization using preference-based surrogate models. Specifically, it presents an integrated clustering-based updating procedure in a genetic algorithm setup to iteratively improve the efficacy of Kriging models. A potential drawback of using preference-based surrogate models in simulation based design is that the surrogates may misrepresent the true optima if the model building schemes fail to capture the critical points of interest with enough fidelity or clarity. This work addresses this vulnerability and presents an efficient clustering-technique integrated surrogate model updating procedure that can capture the buried, transient, yet inherent data pattern in the evolution progression of design candidates within a genetic algorithm setup, and screen out distinct optimal points for subsequent sequential model validation and updating. The results show that the successful finding of the true optimal design through cost-effective surrogate-based optimization depends not only on the selection of sampling schemes such as sample rate and distribution in the initial surrogate model build-up, but also on an efficient and reliable updating procedure that can prevent suboptimal decisions.

Generating Pseudo-Data to Enhance the Performance of Classification-Based Engineering Design: A Preliminary Investigation

2020 ◽  
Author(s):  
Xianping Du ◽  
Onur Bilgen ◽  
Hongyi Xu
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Real World ◽  
Surrogate Model ◽  
Preliminary Investigation ◽  
Learning Model ◽  
Classification Model ◽  
Design Decision ◽  
Large Dataset ◽  
Machine Learning Model

Abstract Machine learning for classification has been used widely in engineering design, for example, feasible domain recognition and hidden pattern discovery. Training an accurate machine learning model requires a large dataset; however, high computational or experimental costs are major issues in obtaining a large dataset for real-world problems. One possible solution is to generate a large pseudo dataset with surrogate models, which is established with a smaller set of real training data. However, it is not well understood whether the pseudo dataset can benefit the classification model by providing more information or deteriorates the machine learning performance due to the prediction errors and uncertainties introduced by the surrogate model. This paper presents a preliminary investigation towards this research question. A classification-and-regressiontree model is employed to recognize the design subspaces to support design decision-making. It is implemented on the geometric design of a vehicle energy-absorbing structure based on finite element simulations. Based on a small set of real-world data obtained by simulations, a surrogate model based on Gaussian process regression is employed to generate pseudo datasets for training. The results showed that the tree-based method could help recognize feasible design domains efficiently. Furthermore, the additional information provided by the surrogate model enhances the accuracy of classification. One important conclusion is that the accuracy of the surrogate model determines the quality of the pseudo dataset and hence, the improvements in the machine learning model.

scholarly journals Impact of Sampling Technique on the Performance of Surrogate Models Generated with Artificial Neural Network (ANN): A Case Study for a Natural Gas Stabilization Unit

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1906 ◽  
Author(s):  
Mohamed Ibrahim ◽  
Saad Al-Sobhi ◽  
Rajib Mukherjee ◽  
Ahmed AlNouss
Keyword(s):  
Monte Carlo ◽  
Surrogate Model ◽  
Surrogate Models ◽  
Average Error ◽  
Illustrative Case ◽  
Sampling Techniques ◽  
Latin Hypercube ◽  
Process Data ◽  
Process Simulator

Data-driven models are essential tools for the development of surrogate models that can be used for the design, operation, and optimization of industrial processes. One approach of developing surrogate models is through the use of input–output data obtained from a process simulator. To enhance the model robustness, proper sampling techniques are required to cover the entire domain of the process variables uniformly. In the present work, Monte Carlo with pseudo-random samples as well as Latin hypercube samples and quasi-Monte Carlo samples with Hammersley Sequence Sampling (HSS) are generated. The sampled data obtained from the process simulator are fitted to neural networks for generating a surrogate model. An illustrative case study is solved to predict the gas stabilization unit performance. From the developed surrogate models to predict process data, it can be concluded that of the different sampling methods, Latin hypercube sampling and HSS have better performance than the pseudo-random sampling method for designing the surrogate model. This argument is based on the maximum absolute value, standard deviation, and the confidence interval for the relative average error as obtained from different sampling techniques.

A Surrogate Model Based Gait Learning for Biped Robot

2013 ◽  
Vol 433-435 ◽  
pp. 138-145
Author(s):  
Ding Sheng Luo ◽  
Yi Wang ◽  
Xi Hong Wu
Keyword(s):  
Surrogate Model ◽  
Learning Algorithm ◽  
Biped Robot ◽  
Gait Pattern ◽  
Simulation Platform ◽  
Model Based ◽  
Simulation Based ◽  
Real Robot ◽  
Gait Learning ◽  
Learning Trials

Gait learning is usually under a so-called simulation based framework, where a simulation platform is firstly setup, and then based on which the gait pattern is learned via some learning algorithm. For the reason that there exist big differences between simulation platform and real circumstances, an additional adapting procedure is always required when learned gait pattern is applied to a real robot. This case turns out to be more critical for a biped robot, because its control appears more difficult than others, such as a quadruped robot. This leads the new scheme that the gait is directly learned on real robot to be attractive. However, under this real robot based learning scheme, most of those learning algorithms that commonly used under simulation based framework appear to be trivial, since they always needs too many learning trials which may wear out the robot hardware. Faced to this situation, in this paper, a surrogate model based gait learning approach for biped robot is proposed. And the experimental results on a real humanoid robot PKU-HR3 show the effectiveness of the proposed approach.

scholarly journals Genetic evolution vs. function approximation: Benchmarking algorithms for architectural design optimization

2018 ◽  
Vol 6 (3) ◽  
pp. 414-428 ◽  
Author(s):  
Thomas Wortmann
Keyword(s):  
Genetic Algorithms ◽  
Design Optimization ◽  
Architectural Design ◽  
Parametric Design ◽  
Fitness Landscape ◽  
Optimization Methods ◽  
Black Box ◽  
Direct Search ◽  
Model Based ◽  
Simulation Based

Abstract This article presents benchmark results from seven simulation-based problems from structural, building energy, and daylight optimization. Growing applications of parametric design and performance simulations in architecture, engineering, and construction allow the harnessing of simulation-based, or black-box, optimization in the search for less resource- and/or energy consuming designs. In architectural design optimization (ADO) practice and research, the most commonly applied black-box algorithms are genetic algorithms or other metaheuristics, to the neglect of more current, global direct search or model-based, methods. Model-based methods construct a surrogate model (i.e., an approximation of a fitness landscape) that they refine during the optimization process. This benchmark compares metaheuristic, direct search, and model-based methods, and concludes that, for the given evaluation budget and problems, the model-based method (RBFOpt) is the most efficient and robust, while the tested genetic algorithms perform poorly. As such, this article challenges the popularity of genetic algorithms in ADO, as well as the practice of using them for one-to-one comparisons to justify algorithmic innovations. Highlights Benchmarks optimization algorithms on structural, energy, and daylighting problems. Benchmarks metaheuristic, direct search, and model-based optimization methods. Challenges the popularity of genetic algorithms in architectural design optimization. Presents model-based methods as a more efficient and reliable alternative.

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