Jiaxiang Yi

Jiaxiang Yi

Postdoctoral Research Assistant at the University of Oxford

Selected research outputs across Bayesian machine learning, uncertainty quantification, multi-fidelity modelling, reliability analysis, and data-driven mechanics.

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21 publications listed

Journal Articles

  1. Weaving intelligence: Thermally drawn multimaterial fibers toward AI-enabled smart textiles Advanced Materials, 2026 Trung, V. D., Yi, J., Nguyen-Duc, H., et al. Weaving intelligence: Thermally drawn multimaterial fibers toward AI-enabled smart textiles. Advanced Materials, e73574.
  2. Single-to-multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: Application to data-driven constitutive modeling Computer Methods in Applied Mechanics and Engineering, 2026 Yi, J., Ferreira, B. P., & Bessa, M. A. (2026). Single-to-multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: Application to data-driven constitutive modeling. Computer Methods in Applied Mechanics and Engineering, 448, 118479. Paper BibTeX
  3. Practical multi-fidelity machine learning: Fusion of deterministic and Bayesian models Computational Mechanics, 2026 Yi, J., Cheng, J., & Bessa, M. A. (2026). Practical multi-fidelity machine learning: Fusion of deterministic and Bayesian models. Computational Mechanics. Paper BibTeX
  4. Optimum-pursuing method for constrained optimization and reliability-based design optimization problems using kriging model Computer Methods in Applied Mechanics and Engineering, 2024 Meng, Z., Kong, L., Yi, J., & Peng, H. (2024). Optimum-pursuing method for constrained optimization and reliability-based design optimization problems using kriging model. Computer Methods in Applied Mechanics and Engineering, 420, 116704.
  5. Cooperative data-driven modeling Computer Methods in Applied Mechanics and Engineering, 2023 Dekhovich, A., Turan, O. T., Yi, J., & Bessa, M. A. (2023). Cooperative data-driven modeling. Computer Methods in Applied Mechanics and Engineering, 417, 116432.
  6. An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization Structural and Multidisciplinary Optimization, 2022 Cheng, J., Lin, Q., & Yi, J. (2022). An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization. Structural and Multidisciplinary Optimization, 65(7), 188.
  7. SBSC+SRU: An error-guided adaptive kriging method for expensive system reliability analysis Structural and Multidisciplinary Optimization, 2022 Yi, J., Cheng, Y., & Liu, J. (2022). SBSC+SRU: An error-guided adaptive kriging method for expensive system reliability analysis. Structural and Multidisciplinary Optimization, 65(5), 134.
  8. A robust optimization method based on previous optimization knowledge Chinese Journal of Ship Research, 2022 Lyv, G., Cheng, Y., Yi, J., & Liu, J. (2022). A robust optimization method based on previous optimization knowledge. Chinese Journal of Ship Research, 17(2), 148-155.
  9. A sequential multi-fidelity surrogate model-assisted contour prediction method for engineering problems with expensive simulations Engineering with Computers, 2022 Liu, J., Yi, J., Zhou, Q., & Cheng, Y. (2022). A sequential multi-fidelity surrogate model-assisted contour prediction method for engineering problems with expensive simulations. Engineering with Computers, 1-19.
  10. A novel fidelity selection strategy-guided multi-fidelity kriging algorithm for structural reliability analysis Reliability Engineering & System Safety, 2022 Yi, J., Cheng, Y., & Liu, J. (2022). A novel fidelity selection strategy-guided multi-fidelity kriging algorithm for structural reliability analysis. Reliability Engineering & System Safety, 219, 108247.
  11. An efficient constrained global optimization algorithm with a clustering-assisted multiobjective infill criterion using gaussian process regression for expensive problems Information Sciences, 2021 Jiang, P., Cheng, Y., Yi, J., & Liu, J. (2021). An efficient constrained global optimization algorithm with a clustering-assisted multiobjective infill criterion using gaussian process regression for expensive problems. Information Sciences, 569, 728-745.
  12. Genetic optimization method of ship strong frame based on sequential surrogate model Chinese Journal of Ship Research, 2021 Wang, J., Wang, Y., Yi, J., et al. (2021). Genetic optimization method of ship strong frame based on sequential surrogate model. Chinese Journal of Ship Research, 16(4).
  13. An active-learning method based on multi-fidelity kriging model for structural reliability analysis Structural and Multidisciplinary Optimization, 2021 Yi, J., Wu, F., Zhou, Q., Cheng, Y., Ling, H., & Liu, J. (2021). An active-learning method based on multi-fidelity kriging model for structural reliability analysis. Structural and Multidisciplinary Optimization, 63, 173-195.
  14. An entropy weight-based lower confidence bounding optimization approach for engineering product design Applied Sciences, 2020 Qian, J., Yi, J., Zhang, J., Cheng, Y., & Liu, J. (2020). An entropy weight-based lower confidence bounding optimization approach for engineering product design. Applied Sciences, 10(10), 3554.
  15. A sequential constraints updating approach for kriging surrogate model-assisted engineering optimization design problem Engineering with Computers, 2020 Qian, J., Yi, J., Cheng, Y., Liu, J., & Zhou, Q. (2020). A sequential constraints updating approach for kriging surrogate model-assisted engineering optimization design problem. Engineering with Computers, 36, 993-1009.
  16. Efficient adaptive kriging-based reliability analysis combining new learning function and error-based stopping criterion Structural and Multidisciplinary Optimization, 2020 Yi, J., Zhou, Q., Cheng, Y., & Liu, J. (2020). Efficient adaptive kriging-based reliability analysis combining new learning function and error-based stopping criterion. Structural and Multidisciplinary Optimization, 62, 2517-2536.

Conference Papers

  1. Cooperative variance estimation and Bayesian neural networks for disentangling aleatoric and epistemic uncertainties Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026 Yi, J. & Bessa, M. A. (2026). Cooperative variance estimation and Bayesian neural networks for disentangling aleatoric and epistemic uncertainties. Proceedings of the 43rd International Conference on Machine Learning (ICML). Paper BibTeX
  2. Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits Proceedings of the AAAI Conference on Artificial Intelligence, 2024 Cheng, J., Xue, B., Yi, J., & Zhang, Q. (2024). Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 11489-11497. Paper BibTeX
  3. rvesimulator: An Automated Representative Volume Element Simulator for Data-Driven Material Discovery AI for Accelerated Materials Design-NeurIPS 2023 Workshop, 2023 Yi, J. & Bessa, M. A. (2023). rvesimulator: An automated representative volume element simulator for data-driven material discovery. AI for Accelerated Materials Design-NeurIPS 2023 Workshop. Paper BibTeX
  4. An adaptive constraint-handling approach for optimization problems with expensive objective and constraints IEEE Congress on Evolutionary Computation (CEC), 2020 Yi, J., Cheng, Y., & Liu, J. (2020). An adaptive constraint-handling approach for optimization problems with expensive objective and constraints. In CEC, IEEE, 1-8.
  5. A fast forecast method based on high and low fidelity surrogate models for strength and stability of stiffened cylindrical shell with variable ribs IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS), 2018 Yi, J., Liu, J., & Cheng, Y. (2018). A fast forecast method based on high and low fidelity surrogate models for strength and stability of stiffened cylindrical shell with variable ribs. In IEEE 8th USYS, 1-6.