Research outputs
Selected research outputs
- Imprecise uncertainty management with uncertain numbers to facilitate trustworthy computations (Conference Paper - 2025)
- Towards robust prediction of material properties for nuclear reactor design under scarce data -- a study in creep rupture property (Preprint - 2024)
- A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes (Journal article - 2023)
- Efficient Interval-Based Uncertainty Quantification for Model Validation and Predictive Capability (Conference Paper - 2026)
2026
Efficient Interval-Based Uncertainty Quantification for Model Validation and Predictive Capability
Chen, Y. L., Ioannou, I., & Ferson, S. (2026). Efficient Interval-Based Uncertainty Quantification for Model Validation and Predictive Capability. In AIAA SCITECH 2026 Forum. American Institute of Aeronautics and Astronautics. doi:10.2514/6.2026-0296
2025
Imprecise uncertainty management with uncertain numbers to facilitate trustworthy computations
Chen, Y., & Ferson, S. (2025). Imprecise uncertainty management with uncertain numbers to facilitate trustworthy computations. In Proceedings of the Python in Science Conference (pp. 207-221). SciPy. doi:10.25080/ahrt5264
An Integrated Uncertainty Quantification and Optimization for solving the 2025 NASA-DNV Challenge
Rocchetta, R., Nespoli, L., Medici, V., Chen, Y., Angelis, M. D., Ochnio, D., . . . Smith, E. (2025). An Integrated Uncertainty Quantification and Optimization for solving the 2025 NASA-DNV Challenge. In 35th European Safety and Reliability Conference (ESREL 2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025) (pp. 399-406). Research Publishing Services. doi:10.3850/978-981-94-3281-3_esrel-sra-e2025-p3804-cd
Verification of Bayesian Physics-Informed Neural Networks
Yang, Z., Chen, Y., & Patelli, E. (2025). Verification of Bayesian Physics-Informed Neural Networks. In 35th European Safety and Reliability Conference (ESREL 2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025) (pp. 314-321). Research Publishing Services. doi:10.3850/978-981-94-3281-3_esrel-sra-e2025-p5695-cd
2024
Towards robust prediction of material properties for nuclear reactor design under scarce data – a study in creep rupture property
Chen, Y., & Patelli, E. (2024). Towards robust prediction of material properties for nuclear reactor design under scarce data – a study in creep rupture property. In Probabilistic Safety Assessment and Management & Asian Symposium on Risk Assessment and Management. Sandai, Japan. doi:10.5281/zenodo.17256135
2023
Robust and informed Deep Learning for bad data with applications in earthquake engineering
Chen, Y. (2023, October 23). Robust and informed Deep Learning for bad data with applications in earthquake engineering. (University of Liverpool).
A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes
Chen, Y., Patelli, E., Edwards, B., & Beer, M. (2023). A Bayesian Augmented-Learning framework for spectral uncertainty quantification of incomplete records of stochastic processes. Mechanical Systems and Signal Processing, 200, 110573. doi:10.1016/j.ymssp.2023.110573
A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data
Chen, Y., Patelli, E., Edwards, B., & Beer, M. (2023). A physics-informed Bayesian framework for characterizing ground motion process in the presence of missing data. EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS. doi:10.1002/eqe.3877
2020
Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity
Chen, Y., Li, T., Zeng, P., Ma, J., Patelli, E., & Edwards, B. (2020). Dynamic and Probabilistic Multi-class Prediction of Tunnel Squeezing Intensity. ROCK MECHANICS AND ROCK ENGINEERING. doi:10.1007/s00603-020-02138-8
2019
New collocation method for stochastic response surface reliability analyses
Zeng, P., Li, T., Chen, Y., Jimenez, R., Feng, X., & Senent, S. (2020). New collocation method for stochastic response surface reliability analyses. ENGINEERING WITH COMPUTERS, 36(4), 1751-1762. doi:10.1007/s00366-019-00793-2
2018
Extension of quasi-Newton approximation-based SORM for series system reliability analysis of geotechnical problems
Zeng, P., Li, T., Jimenez, R., Feng, X., & Chen, Y. (2018). Extension of quasi-Newton approximation-based SORM for series system reliability analysis of geotechnical problems. ENGINEERING WITH COMPUTERS, 34(2), 215-224. doi:10.1007/s00366-017-0536-8
A New Collocation Points Selection Strategy for Stochastic Response Surface Method
Zeng, P., Jimenez, R., Chen, Y., & Li, T. (2018). A New Collocation Points Selection Strategy for Stochastic Response Surface Method. In Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management (pp. 763-768). Research Publishing Services. doi:10.3850/978-981-11-2726-7_ctc304s3mrs06
2017
Application of Quasi-Newton Approximation-Based SORM for System Reliability Analysis of a Layered Soil Slope
Zeng, P., Jimenez, R., Li, T., Chen, Y., & Feng, X. (2017). Application of Quasi-Newton Approximation-Based SORM for System Reliability Analysis of a Layered Soil Slope. In GEO-RISK 2017: RELIABILITY-BASED DESIGN AND CODE DEVELOPMENTS (pp. 111-119). Retrieved from https://www.webofscience.com/