Ikjin Lee

Ikjin Lee

KAIST

H-index: 32

Asia-South Korea

About Ikjin Lee

Ikjin Lee, With an exceptional h-index of 32 and a recent h-index of 27 (since 2020), a distinguished researcher at KAIST, specializes in the field of Simulation-based design under uncertainties.

His recent articles reflect a diverse array of research interests and contributions to the field:

Modified structure of deep neural network for training multi-fidelity data with non-common input variables

A comprehensive multi-fidelity surrogate framework based on Gaussian process for datasets with heterogeneous responses

Surrogate model-based method for reliability-oriented buckling topology optimization under random field load uncertainty

A survey on design optimization of battery electric vehicle components, systems, and management

An effective active learning strategy for reliability-based design optimization under multiple simulation models

Uncertainty‐oriented thermoelastic topology optimization with stress constraint

Consecutive adaptive Kriging method for high-dimensional reliability analysis based on multi-fidelity framework

A novel sampling method for adaptive gradient-enhanced Kriging

Ikjin Lee Information

University

Position

Associate Professor of Mechanical Engineering

Citations(all)

3839

Citations(since 2020)

2570

Cited By

2196

hIndex(all)

32

hIndex(since 2020)

27

i10Index(all)

76

i10Index(since 2020)

70

Email

University Profile Page

Google Scholar

Ikjin Lee Skills & Research Interests

Simulation-based design under uncertainties

Top articles of Ikjin Lee

Modified structure of deep neural network for training multi-fidelity data with non-common input variables

Journal of Mechanical Design

2024/10

Ikjin Lee
Ikjin Lee

H-Index: 23

A comprehensive multi-fidelity surrogate framework based on Gaussian process for datasets with heterogeneous responses

Knowledge-Based Systems

2024/7

Surrogate model-based method for reliability-oriented buckling topology optimization under random field load uncertainty

Structures

2024/5/1

A survey on design optimization of battery electric vehicle components, systems, and management

2024/3

An effective active learning strategy for reliability-based design optimization under multiple simulation models

Structural Safety

2024/3/1

Uncertainty‐oriented thermoelastic topology optimization with stress constraint

International Journal for Numerical Methods in Engineering

2024

Consecutive adaptive Kriging method for high-dimensional reliability analysis based on multi-fidelity framework

Structural and Multidisciplinary Optimization

2024/1

Ikjin Lee
Ikjin Lee

H-Index: 23

A novel sampling method for adaptive gradient-enhanced Kriging

Computer Methods in Applied Mechanics and Engineering

2024/1/1

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review

2023/8/4

A nonlinearity integrated bi-fidelity surrogate model based on nonlinear mapping

Structural and Multidisciplinary Optimization

2023/9

Surrogate-Based Time-Dependent Reliability Analysis for a Digital Twin

Journal of Mechanical Design

2023/6/2

Distribution estimation of Johnson-Cook parameters considering correlation in quasi-static state

International Journal of Mechanical Sciences

2023/4/15

Jeonghwan Choo
Jeonghwan Choo

H-Index: 4

Ikjin Lee
Ikjin Lee

H-Index: 23

A new sampling approach for system reliability-based design optimization under multiple simulation models

Reliability Engineering & System Safety

2023/3/1

Mingyu Lee
Mingyu Lee

H-Index: 0

Ikjin Lee
Ikjin Lee

H-Index: 23

A new framework for efficient sequential sampling-based RBDO using space mapping

Journal of Mechanical Design

2023/3

Ikjin Lee
Ikjin Lee

H-Index: 23

Fe-Ni-Cr Diffusion Barrier for High-temperature Operation of Bi2Te3

Journal of Alloys and Compounds

2023/1

Deep generative tread pattern design framework for efficient conceptual design

Journal of Mechanical Design

2022/7/1

Design for shared autonomous vehicle (SAV) system employing electrified vehicles: Comparison of battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs)

Cleaner Engineering and Technology

2022/6/1

Ikjin Lee
Ikjin Lee

H-Index: 23

Expected system improvement (ESI): A new learning function for system reliability analysis

Reliability Engineering & System Safety

2022/6/1

Kyungeun Lee
Kyungeun Lee

H-Index: 3

Ikjin Lee
Ikjin Lee

H-Index: 23

A bayesian model calibration under insufficient data environment

Structural and Multidisciplinary Optimization

2022/3

Jeonghwan Choo
Jeonghwan Choo

H-Index: 4

Ikjin Lee
Ikjin Lee

H-Index: 23

Statistical Model Calibration and Design Optimization under Aleatory and Epistemic Uncertainty

Reliability Engineering & System Safety

2022/2/27

Jeonghwan Choo
Jeonghwan Choo

H-Index: 4

Ikjin Lee
Ikjin Lee

H-Index: 23

See List of Professors in Ikjin Lee University(KAIST)

Co-Authors

academic-engine