On the use of surrogate models in engineering design optimization and exploration

Palar, Pramudita Satria; Liem, Rhea Patricia; Zuhal, Lavi Rizki; Shimoyama, Koji

doi:10.1145/3319619.3326813

Cited by 23 publications

(14 citation statements)

References 102 publications

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“…To reduce the computational cost of optimization, researchers often replace an accurate but computationally expensive physical model with a quickly computable model for approximation of objective function -the so-called surrogate model [6]. Surrogate modeling is actively used to solve problems from different fields: simulating oil reservoirs for maximizing the total production of oil value and forecasting the most profitable oilfields [7], optimizing of the heatgenerating components in small electronic devices for control of temperature field [8], obtaining the hydrodynamic performance indexes of ship hull form for increasing its strength [9].…”

Section: Related Workmentioning

confidence: 99%

“…It can be seen that implemented genetic operators have a local nature, which leads to the predominance of exploitation over exploration in a utilized algorithm. To deal with the constrained in optimization problem (6), the genetic operators were applied to individuals until the corresponding transformation satisfies the constraints. This procedure is described in more detail in Alg.…”

Section: A Evolutionary Approachmentioning

confidence: 99%

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Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Starodubcev¹,

Nikitin²,

Kalyuzhnaya³

2022

Preprint

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In the paper, a multi-objective evolutionary surrogate-assisted approach for the fast and effective generative design of coastal breakwaters is proposed. To approximate the computationally expensive objective functions, the deep convolutional neural network is used as a surrogate model. This model allows optimizing a configuration of breakwaters with a different number of structures and segments. In addition to the surrogate, an assistant model was developed to estimate the confidence of predictions. The proposed approach was tested on the synthetic water area, the SWAN model was used to calculate the wave heights. The experimental results confirm that the proposed approach allows obtaining more effective (less expensive with better protective properties) solutions than non-surrogate approaches for the same time.

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Section: Related Workmentioning

confidence: 99%

Section: A Evolutionary Approachmentioning

confidence: 99%

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Starodubcev¹,

Nikitin²,

Kalyuzhnaya³

2022

Preprint

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“…Surrogate models (also called metamodels or response surfaces) are computationally cheaper models designed to approximate the dominant features of a complex model, here, the ABM (Blanning, 1975;Regis and Shoemaker, 2005;O'Hagan, 2006;Asher et al, 2015). They have been used extensively in engineering applications (see (Palar et al, 2019) for a review) and weather forecasting [see (Vlahogianni, 2015;Schultz et al, 2021) for recent reviews]. Specifically, we employ model selection to infer an SM directly from both ABM output and experimental data so that we accurately capture aggregate ABM dynamics.…”

Section: Open Access 1 Introductionmentioning

confidence: 99%

SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

Jain

Norton

Prado

et al. 2022

Front. Mol. Biosci.

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Multiscale systems biology is having an increasingly powerful impact on our understanding of the interconnected molecular, cellular, and microenvironmental drivers of tumor growth and the effects of novel drugs and drug combinations for cancer therapy. Agent-based models (ABMs) that treat cells as autonomous decision-makers, each with their own intrinsic characteristics, are a natural platform for capturing intratumoral heterogeneity. Agent-based models are also useful for integrating the multiple time and spatial scales associated with vascular tumor growth and response to treatment. Despite all their benefits, the computational costs of solving agent-based models escalate and become prohibitive when simulating millions of cells, making parameter exploration and model parameterization from experimental data very challenging. Moreover, such data are typically limited, coarse-grained and may lack any spatial resolution, compounding these challenges. We address these issues by developing a first-of-its-kind method that leverages explicitly formulated surrogate models (SMs) to bridge the current computational divide between agent-based models and experimental data. In our approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), we quantify the uncertainty in the relationship between agent-based model inputs and surrogate model parameters, and between surrogate model parameters and experimental data. In this way, surrogate model parameters serve as intermediaries between agent-based model input and data, making it possible to use them for calibration and uncertainty quantification of agent-based model parameters that map directly onto an experimental data set. We illustrate the functionality and novelty of Surrogate Modeling for Reconstructing Parameter Surfaces by applying it to an agent-based model of 3D vascular tumor growth, and experimental data in the form of tumor volume time-courses. Our method is broadly applicable to situations where preserving underlying mechanistic information is of interest, and where computational complexity and sparse, noisy calibration data hinder model parameterization.

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“…In spite of this difficulty, BO techniques have been successfully applied in many fields of engineering, including materials science [35,17], aerospace engineering [15,6,18,21], turbomachinery design [14,2], or even fluid and structural topology optimization [41,27,28,29,22], thanks to a reduction of the problem dimensionality on the representation level. Nevertheless, since the efficiency of these methods decreases strongly with the rising number of design variables, the development of BO approaches able to address high-dimensional problems is crucial for their future applications in industry [37,26].…”

Section: Introductionmentioning

confidence: 99%

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

Raponi¹,

Wang²,

Bujny³

et al. 2020

Preprint

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Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e.g., automated machine learning and design optimization. Built upon a so-called infill-criterion and Gaussian Process regression (GPR), the BO technique suffers from a substantial computational complexity and hampered convergence rate as the dimension of the search spaces increases. Scaling up BO for high-dimensional optimization problems remains a challenging task. In this paper, we propose to tackle the scalability of BO by hybridizing it with a Principal Component Analysis (PCA), resulting in a novel PCA-assisted BO (PCA-BO) algorithm. Specifically, the PCA procedure learns a linear transformation from all the evaluated points during the run and selects dimensions in the transformed space according to the variability of evaluated points. We then construct the GPR model, and the infill-criterion in the space spanned by the selected dimensions. We assess the performance of our PCA-BO in terms of the empirical convergence rate and CPU time on multi-modal problems from the COCO benchmark framework. The experimental results show that PCA-BO can effectively reduce the CPU time incurred on high-dimensional problems, and maintains the convergence rate on problems with an adequate global structure. PCA-BO therefore provides a satisfactory trade-off between the convergence rate and computational efficiency opening new ways to benefit from the strength of BO approaches in high dimensional numerical optimization.

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On the use of surrogate models in engineering design optimization and exploration

Cited by 23 publications

References 102 publications

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

Surrogate-Assisted Evolutionary Generative Design Of Breakwaters Using Deep Convolutional Networks

SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth

High Dimensional Bayesian Optimization Assisted by Principal Component Analysis

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