Dieses Bild zeigt Kai Cheng

Kai Cheng

Herr M.Sc.

Doktorand
Institut für Wasser- und Umweltsystemmodellierung (LS3/SimTech)

Kontakt

+49 711 685 60327
Visitenkarte (VCF)

Pfaffenwaldring 5a
D-70569 Stuttgart
Raum: 2.35

  1. 2021

    1. Cheng K, Xiao S, Zhang X, Oladyshkin S, Nowak W. Resampling method for reliability-based design optimization based on thermodynamic integration and parallel tempering. Mechanical Systems and Signal Processing. 2021;156:107630.

  2. 2019

    1. Cheng K, Lu Z. Time-variant reliability analysis based on high dimensional model representation. Reliability Engineering & System Safety. August 2019;188:310–9.
    2. Cheng K, Lu Z, Chaozhang K. Gradient-enhanced high dimensional model representation via Bayesian inference. Knowledge-Based Systems. 2019;
    3. Cheng K, Lu Z, Zhen Y. Multi-level multi-fidelity sparse polynomial chaos expansion based on Gaussian process regression. Computer Methods in Applied Mechanics and Engineering. Juni 2019;349:360–77.

  3. 2018

    1. Cheng K, Lu Z. Adaptive sparse polynomial chaos expansions for global sensitivity analysis based on support vector regression. Computers & Structures. Januar 2018;194:86–96.
    2. Cheng K, Lu Z. Sparse polynomial chaos expansion based on D-MORPH regression. Applied Mathematics and Computation. April 2018;323:17–30.

  4. 2017

    1. Cheng K, Lu Z, Wei Y, Shi Y, Zhou Y. Mixed kernel support vector regression for global sensitivity analysis. Mechanical Systems and Signal Processing. November 2017;96:201–14.
    2. Cheng K, Lu Z, Zhou Y, Shi Y, Wei Y. Global sensitivity analysis using support vector regression. Applied Mathematical Modelling. September 2017;49:587–98.

07/2015 B.Sc. Technische Mechanik, China University of Petroleum  (China)
02/2018 M.Sc. Flugzeugbau, Schule der Luftfahrt, Northwestern Polytechnical University (China)
03/2018-09/2019 Doktorand, Schule der Luftfahrt, Northwestern Polytechnical University (China)
Seit 09/2019 Doktorand, Institut für Wasser- und Umweltsystemmodellierung, Universität Stuttgart

ResearchGate

Ersatzmodell, Maschinelles Lernen, Quantifizierung von Unsicherheit

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