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FACULTY

Xiao，Sinan
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Xiao，Sinan

  • Title: Associated Professor

  • Email: sinanxiao@scut.edu.cn

  • Working For: Shien-Ming Wu School of Intelligent Engineering

  • Zip Code: 510640

  • Graduated School: Northwestern Polytechnical University，School of Aeronautics

  • Office:

  • Final Degree：Doctor of Engineering

  • Telephone:

  • Mentor Type:

Personal profile  

Sinan Xiao received his Bachelor of Engineering degree in flight vehicle design and engineering and PhD degree in flight vehicle design from the Northwestern Polytechnical University, Xi’an, China, in 2013 and 2018, respectively.

From 2018, Dr. Xiao started his postdoctoral research with the support of the Sino-German (CSC-DAAD) Postdoc Scholarship Program at the University of Stuttgart, Stuttgart, Germany. From 2020, he continued his research at the Forschungszentrum Jülich (FZJ, Jülich Research Centre) under the support of Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). After that, he worked as a Research Associate from 2021 at the University of Bath, Bath, UK. Since 2024, he has joined the South China University of Technology, Guangzhou, China, as an Associate Professor at the Shien-Ming Wu School of Intelligent Engineering.

He has broad interests in uncertainty quantification, structural reliability analysis and design; structural risk assessment; rear event simulation; sensitivity analysis of model output; Markov chain Monte Carlo; Bayesian inference; machine learning; Gaussian process, etc. His research has been focused on:

Gaussian process, etc. His research has been focused on:

  • Structural Reliability Design and Risk Analysis: Combining probability and statistics theory with structural (finite element) models to predict structural reliability, conduct reliability-based optimization design, and evaluate corresponding risks.

  • Bayesian Uncertainty Quantification: Based on prior information and observed data, using Bayesian statistical inference theory to calibrate model parameters, quantify the uncertainty of structural parameters and responses, and provide a basis for intelligent decision-making.

  • Bayesian Experimental Design: Fully considering the role of prior information, integrating it into experimental design and result analysis, gradually updating the model, and achieving the goal of obtaining more information with fewer experimental costs.

  • Sensitivity Analysis of Model Output: Quantifying the impact of model parameters on model output responses, deeply understanding the relationship between input and output variables of the model (system) and quantifying the robustness of the model (system) to uncertainty factors.

Highly self-motivated students and post doctors are welcome to join my team!

Work Experience  

  • 2020.09 - Present, South China University of Technology, Guangzhou, China, Associate Professor

  • 2021.06 - 2024.09，University of Bath, Bath，UK，Research Associate

  • 2020.02 - 2021.03，Forschungszentrum Jülich, Jülich，Germany, Postdoc

  • 2018.08 – 2021.01，University of Stuttgart，Stuttgart，Germany，Postdoc

Education  

  • 2013.09 – 2018.03, Northwestern Polytechnical University, Xi’an, China, Flight Vehicle Design, PhD.

  • 2009.09 – 2013.07, Northwestern Polytechnical University, Xi’an, China, Flight Vehicle Design and Engineering, Bachelor.

Research Interest  

  • Structural reliability design and risk analysis

  • Bayesian uncertainty quantification

  • Bayesian experimental design

  • Sensitivity analysis of model output

Rewards and Awards  

  • 2020 Outstanding Doctoral Dissertation from Northwestern Polytechnical University

  • 2018 Deutscher Akademischer Austauschdienst (DAAD) scholarship

Selected Publications

  • 1. S. Xiao, W. Nowak (2024). Failure probability estimation with failure samples: An extension of the two-stage Markov chain Monte Carlo simulation. Mechanical Systems and Signal Processing. 212, 111300

  • 2. L. Chavez Rodriguez, A. González-Nicolás, B. Ingalls, T. Streck, W. Nowak, S. Xiao, & H. Pagel (2022). Optimal design of experiments to improve the characterisation of atrazine degradation pathways in soil. European Journal of Soil Science, 73(1), e13211

  • 3. S. Xiao, W. Nowak (2022). Reliability sensitivity analysis based on a two-stage Markov chain Monte Carlo simulation. Aerospace Science and Technology, 130, 107938.

  • 4. M. Hinze, S. Xiao, A. Schmidt, W. Nowak (2022). Experimental evaluation and uncertainty quantification for a fractional viscoelastic model of salt concrete. Mechanics of Time-Dependent Materials, 1-24.

  • 5. K. Cheng, Z. Lu, S. Xiao, S. Oladyshkin, W. Nowak (2022) Mixed covariance function kriging model for uncertainty quantification. International Journal for Uncertainty Quantification. 12(3), 17-30.

  • 6. S. Xiao, T. Xu, S. Reuschen, W. Nowak, & H.-J. Hendricks Franssen (2021). Bayesian inversion of multi-Gaussian log-conductivity fields with uncertain hyperparameters: An extension of preconditioned Crank-Nicolson Markov chain Monte Carlo with parallel tempering. Water Resources Research, 57, e2021WR030313.

  • 7. S. Xiao, T. Praditia, S. Oladyshkin, W. Nowak (2021). Global sensitivity analysis of a CaO/Ca(OH)2 thermochemical energy storage model for parametric effect analysis. Applied Energy, 285, 116456.

  • 8. K. Cheng, Z. Lu. S. Xiao, X. Zhang, S. Oladyshkin, W. Nowak (2021). Resampling method for reliability-based design optimization based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing, 156, 107630.

  • 9. S. Xiao, S. Oladyshkin, W. Nowak (2020). Forward-reverse switch between density-based and regional sensitivity analysis. Applied Mathematical Modelling, 84, 377-392.

  • 10. S. Xiao, S. Oladyshkin, W. Nowak (2020). Reliability analysis with stratified importance sampling based on adaptive Kriging. Reliability Engineering & System Safety, 197, 106852.

  • 11. D. Erdal, S. Xiao, W. Nowak, O.A. Cirpka (2020). Sampling behavioral model parameters for ensemble-based sensitivity analysis using Gaussian process emulation and active subspaces. Stochastic Environmental Research and Risk Assessment, 34(11), 1813-1830.

  • 12. S. Xiao, Z. Lu (2020). Structural reliability analysis with conditional importance sampling method based on the law of total expectation and variance in subintervals. Journal of Engineering Mechanics, 146(1), 04019111.

  • 13. S. Xiao, S. Oladyshkin, W. Nowak (2019). Reliability sensitivity analysis with subset simulation: application to a carbon dioxide storage problem. IOP Conference Series: Materials Science and Engineering, 615, 012051.

  • 14. S. Xiao, S. Reuschen, G. Köse, S. Oladyshkin, W. Nowak (2019). Estimation of small failure probabilities based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing, 133, 106248.

  • 15. Y. Wang, S. Xiao, Z. Lu (2019). An efficient method based on Bayes’ theorem to estimate the failure-probability-based sensitivity measure. Mechanical Systems and Signal Processing, 115, 607-620.

  • 16. L. Xu, Z. Lu, S. Xiao (2019). Generalized sensitivity indices based on vector projection for multivariate output. Applied Mathematical Modelling, 66, 592-610.

  • 17. Y. Zhou, Z. Lu, S. Xiao, W. Yun (2019). Distance correlation-based method for global sensitivity analysis of models with dependent inputs. Structural and Multidisciplinary Optimization, 60, 1189-1207

  • 18. S. Xiao, Z. Lu, P. Wang (2018). Multivariate global sensitivity analysis based on distance components decomposition. Risk Analysis, 38(12), 2703-2721.

  • 19. S. Xiao, Z. Lu (2018). Global sensitivity analysis based on Gini’s mean difference. Structural and Multidisciplinary Optimization, 58(4), 1523-1535

  • 20. S. Xiao, Z. Lu, P. Wang (2018). Global sensitivity analysis based on distance correlation for structural systems with multivariate output. Engineering Structures, 167, 74-83.

  • 21. Y. Wang, S. Xiao, Z. Lu (2018). A new efficient simulation method based on Bayes' theorem and importance sampling Markov chain simulation to estimate the failure-probability-based global sensitivity measure. Aerospace Science and Technology, 79, 364-372.

  • 22. S. Xiao, Z. Lu, L. Xu (2018). Global sensitivity analysis based on random variables with interval parameters by metamodel-based optimization. International Journal of Systems Science: Operations & Logistics 5(3), 268-281.

  • 23. S. Xiao, Z. Lu (2018). Reliability analysis by combining higher-order unscented transformation and fourth-moment method. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 4(1), 04017034.

  • 24. S. Xiao, Z. Lu, P. Wang (2018). Multivariate global sensitivity analysis for dynamic models based on energy distance. Structural and Multidisciplinary Optimization. 57(1), 279-291.

  • 25. P. Wang, Z. Lu, K. Zhang, S. Xiao, Z. Yue (2018). Copula-based decomposition approach for the derivative-based sensitivity of variance contributions with dependent variables. Reliability Engineering & System Safety, 169, 437-450

  • 26. S. Xiao, Z. Lu, P. Wang (2018). Multivariate global sensitivity analysis for dynamic models based on wavelet analysis. Reliability Engineering & System Safety, 170, 20-30.

  • 27. S. Xiao, Z. Lu (2017). Structural reliability sensitivity analysis based on classification of model output. Aerospace Science and Technology, 71, 52-61.

  • 28. S. Xiao, Z. Lu, L. Xu (2017). Multivariate sensitivity analysis based on the direction of eigen-space through principal component analysis. Reliability Engineering & System Safety, 165, 1-10.

  • 29. S. Xiao, Z. Lu, F. Qin (2017). Estimation of the Generalized Sobol’s Sensitivity Index for Multivariate Output Model Using Unscented Transformation. Journal of Structural Engineering, 143(5), 06016005.

  • 30. P. Wang, Z. Lu, S. Xiao (2017). A generalized separation for the variance contributions of input variables and their distribution parameters. Applied Mathematical Modelling, 47, 381-399.

  • 31. P. Wang, Z. Lu, S. Xiao (2017). Variance-based sensitivity analysis with the uncertainties of the input variables and their distribution parameters. Communication in Statistics- Simulation and Computation, 47(4), 1103-1125.

  • 32. S. Xiao, Z. Lu, L. Xu (2016). A new effective screening design for structural sensitivity analysis of failure probability with the epistemic uncertainty. Reliability Engineering & System Safety, 156, 1-14.

  • 33. S. Xiao, Z. Lu (2016). Structural Reliability Analysis Using Combined Space Partition Technique and Unscented Transformation. Journal of Structural Engineering, 142(11), 04016089.

Patents  

  • A method for calculating the probability of structural failure based on optimal conditional importance sampling, China Patent, 2022-11-04, ZL201811029879.7.