Feasibility classification of new design points using support vector machine trained by reduced dataset

Jeong, Seung-Hyun; Choi, Donghoon; Jeong, Minjoong

doi:10.1007/s12541-012-0096-1

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Specifically, s controls the outcome prediction uncertainty impact, o the optimization target prediction impact and r the distance to the nearest neighbor impact, respectively, Eq. (12). Setups ( 1) to (4) use our algorithm, whereas setup (5) represents a pure grid approach in the framework of our algorithm, which is finished after the initial sampling step since…”

Section: Discussionmentioning

confidence: 99%

“…The utility vector consists of three components with a distinct meaning, Eq. (12). These components are weighted by the weights s, o and r contained in the weight vector.…”

Section: Utility Functionmentioning

confidence: 99%

“…For example, in Ref. [12] parameters are classified according to their feasibility using a support vector machine (SVM) [9] combined with k-means clustering to reduce the model training cost.…”

Section: Introductionmentioning

confidence: 99%

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Optimized data exploration applied to the simulation of a chemical process

Heese

Walczak

Seidel

et al. 2019

Computers & Chemical Engineering

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In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class describing whether or not they lead to valid results. In general, it can be very difficult to determine feasible parameter regions, especially without previous knowledge. We propose a novel algorithm to explore such an unknown parameter space and improve its feasibility classification in an iterative way. Moreover, we include an additional optimization target in the algorithm to guide the exploration towards regions of interest and to improve the classification therein. In our method we make use of well-established concepts from the field of machine learning like kernel support vector machines and kernel ridge regression. From a comparison with a Kriging-based exploration approach based on recently published results we can show the advantages of our algorithm in a binary feasibility classification scenario with a discrete feasibility constraint violation. In this context, we also propose an improvement of the Kriging-based exploration approach. We apply our novel method to a fully realistic, industrially relevant chemical process simulation to demonstrate its practical usability and find a comparably good approximation of the data space topology from relatively few data points.

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Section: Discussionmentioning

confidence: 99%

“…The utility vector consists of three components with a distinct meaning, Eq. (12). These components are weighted by the weights s, o and r contained in the weight vector.…”

Section: Utility Functionmentioning

confidence: 99%

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Optimized data exploration applied to the simulation of a chemical process

Heese

Walczak

Seidel

et al. 2019

Computers & Chemical Engineering

View full text Add to dashboard Cite

show abstract

“…In Ding and Vemur (2005), an active learning strategy for feasibility classification of analog circuits was implemented. Jeong et al (2012) used a support vector machine classifier for mathematical test problems and air-conditioner pipe design. In the context of material design, Jung et al (2019) modeled feasibility constraints to carry out a material optimization for inverse crystallographic texture problems.…”

Section: Introductionmentioning

confidence: 99%

Decomposition and optimization of linear structures using meta models

Krischer

Zimmermann

2021

Struct Multidisc Optim

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Monolithic optimization of large mechanical systems can be expensive and cumbersome. Drivers of computational cost and integration effort are, e.g., the size of the design problem and the number of different components, models, and disciplines. Distributed optimization schemes decompose large problems into smaller subproblems; however, they typically require intense coordination effort. This paper proposes an approach for complete decoupling by decomposing a monolithic optimization into independent optimization subproblems that can be solved without need for coordination. This is accomplished by sampling the space of component performance, here represented by eigenvalues and eigenvectors of stiffness matrices, and establishing meta models that map the relevant component performance values onto feasibility and mass estimates. The optimization procedure consists of two steps: First, a system optimization problem is solved by assigning stiffness requirements to components that are approximately feasible and mass-optimal. Second, the component optimization problems are solved independently of each other such that stiffness requirements are satisfied. As information on feasibility and mass is provided during system optimization by meta models, the approach will be referred to as informed decomposition. The effectiveness of the approach is demonstrated by minimizing the mass of a simple two-component linear structure subject to a requirement on total stiffness. This is done for three different component models, a beam with constant cross-section, a beam with varying cross-sections, and an arbitrary 2-dimensional body, using parametric and topology optimization, respectively. The approach produces results that are at most 1 % heavier than the results obtained by monolithic optimization.

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Support vector machine based optimal control for minimizing energy consumption of biped walking motions

Wang

Chen

et al. 2012

Int. J. Precis. Eng. Manuf.

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Feasibility classification of new design points using support vector machine trained by reduced dataset

Cited by 9 publications

References 8 publications

Optimized data exploration applied to the simulation of a chemical process

Optimized data exploration applied to the simulation of a chemical process

Decomposition and optimization of linear structures using meta models

Support vector machine based optimal control for minimizing energy consumption of biped walking motions

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