Kai Cheng

Kai Cheng is a Humboldt Research Fellow at the Engineering Risk Analysis Group, Technical University of Munich (TUM), since November 2023. His work focuses on developing computational methods for reliability assessment and uncertainty quantification in engineering systems.

Education:

• Ph.D. in Flight Vehicle Design, Northwestern Polytechnical University (2018–2021)
• M.Sc. in Flight Vehicle Design, Northwestern Polytechnical University (2015–2018)
• B.Sc. in Mechanics, China University of Petroleum (East China) (2011–2015)

Research Interests:

Dr. Cheng's research integrates stochastic dynamics, machine learning, and model reduction techniques to address challenges in reliability analysis. Key areas include surrogate modeling for high-dimensional systems, rare event probability estimation, and adaptive algorithms for uncertainty quantification in mechanical and structural applications.

Publication Trends:

His recent articles emphasize machine learning-enhanced reliability methods, including gradient-based Kriging, Bayesian regression, and Monte Carlo variants. Dominant themes are computational efficiency in high-dimensional problems and adaptive algorithms for structural safety.

Awards:

• Humboldt Research Fellowship (2023–present)

Teaching:

At TUM, he assists in graduate courses on Stochastic Finite Element Methods and Estimation of Rare Events and Failure Probabilities.

Team Affiliation:

He collaborates with the Engineering Risk Analysis Group at TUM, which specializes in probabilistic risk assessment for infrastructure and dynamical systems.