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Gang Li

Researcher at Dalian University of Technology

Publications -  124
Citations -  2673

Gang Li is an academic researcher from Dalian University of Technology. The author has contributed to research in topics: Nonlinear system & Finite element method. The author has an hindex of 24, co-authored 112 publications receiving 1797 citations.

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A new directional stability transformation method of chaos control for first order reliability analysis

TL;DR: In this article, the authors derived the formulation of the Lyapunov exponents for the first order reliability method (FORM) iterative algorithm in order to identify these complicated numerical instability phenomena of discrete chaotic dynamic systems.
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A Comparative Study of Metaheuristic Algorithms for Reliability-Based Design Optimization Problems

TL;DR: This study presents a comprehensive work on the application of ten popular and recent metaheuristic algorithms of five engineering problems and presents the state-of-the-art in RBDO about its global convergence, robustness, accuracy, and computational speed.
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An integrated framework of exact modeling, isogeometric analysis and optimization for variable-stiffness composite panels

TL;DR: An integrated framework of exact modeling, isogeometric analysis and optimization for variable-stiffness panels is developed for the global optimum, and the proposed method is able to provide a more efficient optimum design with significant less computational cost compared to other traditional methods.
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Efficient Optimization of Cylindrical Stiffened Shells with Reinforced Cutouts by Curvilinear Stiffeners

TL;DR: In this article, an efficient optimization framework of cylindrical stiffened shells with reinforced cutouts by curvilinear stiffeners is proposed, where the numerical implementation asymptotic homogenization method is used to smear out the stiffeners in the far field.
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An importance learning method for non-probabilistic reliability analysis and optimization

TL;DR: A novel importance learning method (ILM) is proposed on the basis of active learning technique using Kriging metamodel, which builds the Kriged model accurately and efficiently by considering the influence of the most concerned point.