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Publications by Ikjin Lee
Conservative Surrogate Model Using Weighted Kriging Variance for Sampling-Based RBDO
Journal of Mechanical Design, Transactions Of the ASME
Computer Science Applications
Mechanics of Materials
Computer Graphics
Computer-Aided Design
Mechanical Engineering
Erratum to “Adaptive Single-Loop Reliability-Based Design Optimization and Post Optimization Using Constraint Boundary Sampling”
Journal of Mechanical Science and Technology
Mechanics of Materials
Mechanical Engineering
Related publications
Conservative Predictions Using Surrogate Modeling
Optimization of Chemical Processes Using Surrogate Models Based on a Kriging Interpolation
Computer Aided Chemical Engineering
Kriging-Sparse Polynomial Dimensional Decomposition Surrogate Model With Adaptive Refinement
Journal of Computational Physics
Numerical Analysis
Applied Mathematics
Simulation
Computer Science Applications
Modeling
Computational Mathematics
Astronomy
Physics
Exploration Versus Exploitation Using Kriging Surrogate Modelling in Electromagnetic Design
COMPEL - The International Journal for Computation and Mathematics in Electrical and Electronic Engineering
Electronic Engineering
Applied Mathematics
Computer Science Applications
Electrical
Computational Theory
Mathematics
Kriging Surrogate Model for Resonance Frequency Analysis of Dental Implants by a Latin Hypercube-Based Finite Element Method
Applied Bionics and Biomechanics
Medicine
Bioengineering
Biomedical Engineering
Biotechnology
Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging
Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging
A Kriging Surrogate Model for the Interference Reduction in the Settlement Surveillance Sensors of Steel Transmission Towers
Applied Sciences (Switzerland)
Instrumentation
Materials Science
Fluid Flow
Engineering
Computer Science Applications
Process Chemistry
Transfer Processes
Technology
A Multiobjective Adaptive Surrogate Modeling-Based Optimization (MO-ASMO) Framework Using Efficient Sampling Strategies
