Education

Education

Oct. 2021 -

PhD candidate at ME-MSE, TU Delft.

• Promotor: Marcel Sluiter
• Daily Supervisor : Miguel Bessa
• Research interests: Multi-scale simulation, Bayesian deep learning, Multi-fidelity modeling

Sep. 2018 - Jun. 2021

Huazhong University of Science and Technology

• Master of Science: Design and Construction of Naval Architecture and Ocean Structure
• GPA: 3.91/4.0
• Master’s thesis: Improved sequential Kriging-Monte Carlo Simulation reliability analysis methods and their applications to ship structural design

Sep. 2014 - Jun. 2018

Huazhong University of Science and Technology

• Bachelor of Engineering: Naval Architecture and Ocean Engineering
• GPA: 3.81/4.0
• Thesis: Strength and stability prediction for cylindical shell with variable ribs via multi-fidelity surrogate model

Publications

1 J. Yi, J. Cheng, M. Bessa. “Practical multi-fidelity machine learning: fusion of deterministic and Bayesian models”, arXiv preprint arXiv:2407.15110

2 J. Yi, Y. Cheng, J. Liu. “SBSC+ SRU: an error-guided adaptive Kriging method for expensive system reliability analysis ,” Structural and Multidisciplinary Optimization, 65(5):1-18, 2022

3 J. Yi, Y. Cheng, J. Liu. “A novel fidelity selection strategy-guided multi-fidelity kriging algorithm for structural reliability analysis ,” Reliability Engineering & System Safety, 219, 108247, 2022.

4 J. Cheng, Q. Lin, J. Yi. “An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization,” Structural and Multidisciplinary Optimization, 65(7):1-21, 2022.

5 J. Yi, F. Wu, Y. Cheng, et al. “An active-learning method based on multi-fidelity Kriging model for structural reliability analysis ,” Structural and Multidisciplinary Optimization, 63(1):173-195, 2021.

6 J. Yi, Q, Zhou, Y. Cheng, et al. “Efficient adaptive Kriging-based reliability analysis combining new learning function and error-based stopping criterion,” Structural and Multidisciplinary Optimization, 62(5):2517-2536, 2020

7 J. Liu, J. Yi, Q, Zhou, et al. “A sequential multi-fidelity surrogate model-assisted contour prediction method for engineering problems with expensive simulations,” Engineering with Computers, 2020.

A list is also available online

Presentations

2024

• SIAM UQ 2024: Bayesian Neural Networks Predicting Aleatoric and Epistemic Uncertainties

• ECCOMAS 2024: Bayesian design of recycled composite polymers with predictable uncertain behavior

• SES 2024: Learning Uncertainty-Aware Composite Constitutive Laws via Bayesian Recurrent Neural Network

2023

• SC17-008: A Tutorial on the Framework for Data-Driven Design and Analysis of Structures and Materials

2020

• “An adaptive constraint-handling approach for optimization problems with expensive objective and constraints ,” 2020 IEEE Congress on Evolutionary Computation (CEC)

2018

• “A fast forecast method based on high and low fidelity surrogate models for strength and stability of stiffened cylindrical shell with variable ribs,” 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS), 2018

Peer Review

• Reliability engineering and system safety
• Applied Soft Computing
• Engineering With Computers
• Information Sciences
• Probabilistic Engineering Mechanics

Selcted Awards

2021

• Outstanding Master Graduates, HUST

2020

• National Scholarship for Postgraduates
• Merit Master Student of HUST

2018

• Outstanding Graduates of HUST

2017

• Merit Undergraduate Student of HUST
• National Encouragement Scholarship

2016

• National Encouragement Scholarship
• Excellent Student Cadre of HUST