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David Moens

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2020 – today

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2024
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  - journals/ress/YangZLFMB24
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Lechang Yang, Xinyao Zhang, Zitong Lu, Yuqiang Fu, David Moens, Michael Beer:
Reliability evaluation of a multi-state system with dependent components and imprecise parameters: A structural reliability treatment. Reliab. Eng. Syst. Saf. 250: 110240 (2024)
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  - journals/corr/abs-2401-05043
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Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal, Fabio Cuzzolin, David Moens, Hans Hallez:
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks. CoRR abs/2401.05043 (2024)
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  - journals/corr/abs-2405-15047
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Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez:
Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection. CoRR abs/2405.15047 (2024)
2022
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Keivan Shariatmadar, Kaizheng Wang, Calvin R. Hubbard, Hans Hallez, David Moens:
An introduction to optimization under uncertainty - A short survey. CoRR abs/2212.00862 (2022)
2021
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  - journals/tbe/BartsoenFWWMJS21
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Laura Bartsoen, Matthias G. R. Faes, Mariska Wesseling, Roel Wirix-Speetjens, David Moens, Ilse Jonkers, Jos Vander Sloten:
Computationally Efficient Optimization Method to Quantify the Required Surgical Accuracy for a Ligament Balanced TKA. IEEE Trans. Biomed. Eng. 68(11): 3273-3280 (2021)

2010 – 2019

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2019
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Matthias G. R. Faes, Ghosh Dastidar Sabyasachi, David Moens:
Hybrid spatial uncertainty analysis for the estimation of imprecise failure probabilities in Laser Sintered PA-12 parts. Comput. Math. Appl. 78(7): 2395-2406 (2019)
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Lars Bogaerts, Matthias G. R. Faes, David Moens:
A fast inverse approach for the quantification of set-theoretical uncertainty. SSCI 2019: 768-775
2017
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Matteo Broggi, Matthias G. R. Faes, Edoardo Patelli, Yves Govers, David Moens, Michael Beer:
Comparison of Bayesian and interval uncertainty quantification: Application to the AIRMOD test structure. SSCI 2017: 1-8

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