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publications
A fast forecast method based on high and low fidelity surrogate models for strength and stability of stiffened cylindrical shell with variable ribs
Published in IEEE 8th International Conference on Underwater System Technology: Theory and Applications (USYS), 2018
A fast forecast method based on high- and low-fidelity surrogate models for stiffened cylindrical shells.
Recommended citation: 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.
Efficient adaptive kriging-based reliability analysis combining new learning function and error-based stopping criterion
Published in Structural and Multidisciplinary Optimization, 2020
An adaptive kriging-based reliability analysis method combining a learning function and an error-based stopping criterion.
Recommended citation: 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.
A sequential constraints updating approach for kriging surrogate model-assisted engineering optimization design problem
Published in Engineering with Computers, 2020
A sequential constraints updating approach for kriging surrogate model-assisted engineering optimization.
Recommended citation: 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.
An adaptive constraint-handling approach for optimization problems with expensive objective and constraints
Published in IEEE Congress on Evolutionary Computation (CEC), 2020
An adaptive constraint-handling approach for expensive constrained optimization problems.
Recommended citation: 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.
An entropy weight-based lower confidence bounding optimization approach for engineering product design
Published in Applied Sciences, 2020
An entropy weight-based lower confidence bounding optimization approach for engineering product design.
Recommended citation: 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.
An active-learning method based on multi-fidelity kriging model for structural reliability analysis
Published in Structural and Multidisciplinary Optimization, 2021
An active-learning method based on a multi-fidelity kriging model for structural reliability analysis.
Recommended citation: 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.
Genetic optimization method of ship strong frame based on sequential surrogate model
Published in Chinese Journal of Ship Research, 2021
A genetic optimization method for ship strong frames based on sequential surrogate modeling.
Recommended citation: 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).
An efficient constrained global optimization algorithm with a clustering-assisted multiobjective infill criterion using gaussian process regression for expensive problems
Published in Information Sciences, 2021
A constrained global optimization algorithm with clustering-assisted multiobjective infill criteria for expensive problems.
Recommended citation: 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.
A novel fidelity selection strategy-guided multi-fidelity kriging algorithm for structural reliability analysis
Published in Reliability Engineering & System Safety, 2022
A fidelity selection strategy-guided multi-fidelity kriging algorithm for structural reliability analysis.
Recommended citation: 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.
A sequential multi-fidelity surrogate model-assisted contour prediction method for engineering problems with expensive simulations
Published in Engineering with Computers, 2022
A sequential multi-fidelity surrogate model-assisted contour prediction method for expensive engineering simulations.
Recommended citation: 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.
A robust optimization method based on previous optimization knowledge
Published in Chinese Journal of Ship Research, 2022
A robust optimization method based on previous optimization knowledge.
Recommended citation: 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.
SBSC+SRU: An error-guided adaptive kriging method for expensive system reliability analysis
Published in Structural and Multidisciplinary Optimization, 2022
An error-guided adaptive kriging method for expensive system reliability analysis.
Recommended citation: 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.
An enhanced variable-fidelity optimization approach for constrained optimization problems and its parallelization
Published in Structural and Multidisciplinary Optimization, 2022
An enhanced variable-fidelity optimization approach for constrained optimization problems and parallel implementation.
Recommended citation: 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.
Cooperative data-driven modeling
Published in Computer Methods in Applied Mechanics and Engineering, 2023
A cooperative data-driven modeling framework.
Recommended citation: Dekhovich, A., Turan, O. T., Yi, J., & Bessa, M. A. (2023). Cooperative data-driven modeling. Computer Methods in Applied Mechanics and Engineering, 417, 116432.
rvesimulator: An Automated Representative Volume Element Simulator for Data-Driven Material Discovery
Published in AI for Accelerated Materials Design-NeurIPS 2023 Workshop, 2023
rvesimulator is a Python-Abaqus framework for automating RVE simulations to support large-scale material data generation.
Recommended citation: 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.
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Optimum-pursuing method for constrained optimization and reliability-based design optimization problems using kriging model
Published in Computer Methods in Applied Mechanics and Engineering, 2024
An optimum-pursuing kriging method for constrained optimization and reliability-based design optimization.
Recommended citation: 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.
Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits
Published in Proceedings of the AAAI Conference on Artificial Intelligence, 2024
This paper introduces mixed Pareto-lexicographic orders for multi-objective stochastic linear bandits.
Recommended citation: 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.
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Practical multi-fidelity machine learning: Fusion of deterministic and Bayesian models
Published in Computational Mechanics, 2026
A practical multi-fidelity strategy for fusing deterministic and Bayesian models across low- and high-fidelity data.
Recommended citation: Yi, J., Cheng, J., & Bessa, M. A. (2026). Practical multi-fidelity machine learning: Fusion of deterministic and Bayesian models. Computational Mechanics.
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Single-to-multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: Application to data-driven constitutive modeling
Published in Computer Methods in Applied Mechanics and Engineering, 2026
This work generalizes data-driven learning to history-dependent multi-fidelity settings with uncertainty quantification and uncertainty disentanglement.
Recommended citation: 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.
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Cooperative variance estimation and Bayesian neural networks for disentangling aleatoric and epistemic uncertainties
Published in Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026
A cooperative training strategy that combines variance estimation with Bayesian neural networks to disentangle aleatoric and epistemic uncertainties.
Recommended citation: 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).
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Weaving intelligence: Thermally drawn multimaterial fibers toward AI-enabled smart textiles
Published in Advanced Materials, 2026
Thermally drawn multimaterial fibers toward AI-enabled smart textiles.
Recommended citation: Trung, V. D., Yi, J., Nguyen-Duc, H., et al. Weaving intelligence: Thermally drawn multimaterial fibers toward AI-enabled smart textiles. Advanced Materials, e73574.
talks
A fast forecast method based on high and low fidelity surrogate models for strength and stability of stiffened cylindrical shell with variable ribs
Published:
Conference talk on high- and low-fidelity surrogate models for strength and stability prediction of stiffened cylindrical shells with variable ribs.
An adaptive constraint-handling approach for optimization problems with expensive objective and constraints
Published:
Conference talk on adaptive constraint handling for optimization problems with expensive objectives and constraints.
Cooperative Data-Driven Modeling
Published:
Invited talk on cooperative data-driven modeling.
Practical Multi-fidelity Bayesian Machine Learning
Published:
Invited talk at the Overseas Young Scholars Forum on practical multi-fidelity Bayesian machine learning.
Practical Multi-fidelity Bayesian Machine Learning
Published:
Invited talk on practical multi-fidelity Bayesian machine learning.
Learning uncertainty-aware composite constitutive laws via Bayesian Recurrent Neural Network
Published:
Cooperative variance estimation and Bayesian neural networks disentangle aleatoric and epistemic uncertainties
Published:
Invited seminar on cooperative variance estimation and Bayesian neural networks for uncertainty disentanglement.
Scalable Bayesian Machine Learning for Sustainable Composite Design: Uncertainty-Aware Prediction and Optimization of Recycled Polypropylene/Polyethylene
Published:
Invited talk on scalable Bayesian machine learning for uncertainty-aware prediction and optimization of recycled polypropylene/polyethylene.
Disentangling Uncertainty in AI for Engineering: Variance estimation Bayesian Neural Networks (VeBNNs)
Published:
Poster presentation on variance estimation Bayesian neural networks for uncertainty disentanglement in engineering AI.
Variance estimation Bayesian Neural Networks (VeBNNs): Cooperative training for disentangling aleatoric and epistemic uncertainties
Published:
Poster presentation at ICML 2026 on variance estimation Bayesian neural networks for disentangling aleatoric and epistemic uncertainties.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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