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Ikjin Lee
Researcher at KAIST
Publications - 144
Citations - 3225
Ikjin Lee is an academic researcher from KAIST. The author has contributed to research in topics: Surrogate model & Monte Carlo method. The author has an hindex of 25, co-authored 127 publications receiving 2397 citations. Previous affiliations of Ikjin Lee include University of Connecticut & University of Iowa.
Papers
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Metamodeling Method Using Dynamic Kriging for Design Optimization
TL;DR: The dynamic kriging method generates a more accurate surrogate model than other metamodeling methods and is applied to the simulation-based design optimization with multiple efficiency strategies.
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Dimension reduction method for reliability-based robust design optimization
TL;DR: In this paper, a reliability-based robust design optimization method is developed using DRM and compared to PMI and PDM for accuracy and efficiency, and the numerical results show that DRM is effective when the number of random variables is small, whereas PMI is more effective when a relatively large number of variables is relatively large.
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Inverse analysis method using MPP-based dimension reduction for reliability-based design optimization of nonlinear and multi-dimensional systems
TL;DR: In this article, the authors proposed an inverse reliability analysis method that can be used to obtain accurate probability of failure calculation without requiring the second-order sensitivities for reliability-based design optimization (RBDO) of nonlinear and multi-dimensional systems.
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Deep Generative Design: Integration of Topology Optimization and Generative Models
TL;DR: In this article, an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance is proposed.
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Sampling-based RBDO using the stochastic sensitivity analysis and Dynamic Kriging method
TL;DR: New efficiency and accuracy strategies such as a hyper-spherical local window for surrogate model generation, sample reuse, local window enlargement, filtering of constraints, and an adaptive initial point for the pattern search are proposed.