Jingwen Song

About Jingwen Song

Jingwen Song, With an exceptional h-index of 17 and a recent h-index of 16 (since 2020), a distinguished researcher at Leibniz Universität Hannover, specializes in the field of Risk and reliability analysis, uncertainty quantification, machine learning, decision-making under uncertainty, applications to.

His recent articles reflect a diverse array of research interests and contributions to the field:

Combined anisotropic and cyclic constitutive model for laser powder bed fusion fabricated aluminum alloy

Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis

Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Structural reliability analysis by line sampling: A Bayesian active learning treatment

Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective

Estimation of small failure probabilities by partially Bayesian active learning line sampling: Theory and algorithm

Bayesian active learning approach for estimation of empirical copula-based moment-independent sensitivity indices

Investigating regional effects of random inputs for multivariate outputs with generalized variance

Jingwen Song Information

University

Position

Doctor of Philosophy in Engineering

Citations(all)

1112

Citations(since 2020)

935

Cited By

518

hIndex(all)

17

hIndex(since 2020)

16

i10Index(all)

19

i10Index(since 2020)

18

Email

University Profile Page

Google Scholar

Jingwen Song Skills & Research Interests

Risk and reliability analysis

uncertainty quantification

machine learning

decision-making under uncertainty

applications to

Top articles of Jingwen Song

Combined anisotropic and cyclic constitutive model for laser powder bed fusion fabricated aluminum alloy

Chinese Journal of Aeronautics

2024/3/12

Constrained Bayesian optimization algorithms for estimating design points in structural reliability analysis

Reliability Engineering & System Safety

2024/1/1

Collaborative and Adaptive Bayesian Optimization for bounding variances and probabilities under hybrid uncertainties

Computer Methods in Applied Mechanics and Engineering

2023/12/1

Structural reliability analysis by line sampling: A Bayesian active learning treatment

Structural Safety

2023/9/1

Combining data and physical models for probabilistic analysis: A Bayesian Augmented Space Learning perspective

Probabilistic Engineering Mechanics

2023/7/1

Estimation of small failure probabilities by partially Bayesian active learning line sampling: Theory and algorithm

Computer Methods in Applied Mechanics and Engineering

2023/7/1

Bayesian active learning approach for estimation of empirical copula-based moment-independent sensitivity indices

Engineering with Computers

2023/6/28

Investigating regional effects of random inputs for multivariate outputs with generalized variance

2023/1/1

Jingwen Song
Jingwen Song

H-Index: 10

Yueqiang Zhang
Yueqiang Zhang

H-Index: 7

Bayesian optimization-aided line sampling for estimating small failure probabilities

2023/1/1

A distributionally robust approach for mixed aleatory and epistemic uncertainties propagation

AIAA Journal

2022/7

Data-driven and active learning of variance-based sensitivity indices with Bayesian probabilistic integration

Mechanical Systems and Signal Processing

2022/1/15

Estimation of failure probability function under imprecise probabilities by active learning–augmented probabilistic integration

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering

2021/12/1

Failure probability estimation of a class of series systems by multidomain Line Sampling

Reliability Engineering & System Safety

2021/9/1

Adaptive reliability analysis for rare events evaluation with global imprecise line sampling

Computer Methods in Applied Mechanics and Engineering

2020/12/1

Active learning line sampling for rare event analysis

Mechanical Systems and Signal Processing

2021/1/15

Non-intrusive imprecise stochastic simulation by line sampling

Structural Safety

2020/5/1

Efficient propagation of imprecise probability models by imprecise line sampling

2020

Stochastic simulation methods for structural reliability under mixed uncertainties

2020

Jingwen Song
Jingwen Song

H-Index: 10

See List of Professors in Jingwen Song University(Leibniz Universität Hannover)

Co-Authors

academic-engine