Jiaxiang Yi bio photo

Jiaxiang Yi

PhD candidate at 3ME-MSE TUdelft

Email Google Scholar LinkedIn Github ResearchGate

Research

Journal Publications

[1] 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

[2] 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.

[3] 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.

[4] 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.

[5] 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

[6] 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

Conference Publications

[1] J. Yi, Y. Cheng., & J. Liu. (2020, July). An adaptive constraint-handling approach for optimization problems with expensive objective and constraints. In 2020 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). IEEE.

[2] J. Yi, Y. Cheng., & J. Liu. (2018, December). A fast forecast method based on high and low fidelity surrogate models for strength and stability of stiffened cylindrical shell with variable ribs. In 2018 IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS) (pp. 1-6). IEEE.

[3] J.Yi, M. Bessa. rvesimulator: An automated representative volume element simulator for data-driven material discovery. AI for Accelerated Materials Design - NeurIPS 2023 Workshop. New Orleans, Louisiana, United States.

Peer Review

Feb. 2021 - Present

  • Reliability Engineering and System Safety
  • Applied Soft Computing