E-LIB Suche - Ergebnisse für: Faes, Matthias

1

Quantifying the epistemic uncertainty linked to the develop..:

Ascia, Paolo ; Faes, Matthias ; Duddeck, Fabian
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2023

Link: https://zenodo.org/recor..

2

First-passage probability estimation of high-dimensional no..:

Ding, Chen ; Dang, Chao ; Valdebenito, Marcos...
DOI:https://doi.org/10.1016/j.ymssp.2022.109775. , 2022

Link: https://www.repo.uni-han..

3

First-passage probability estimation of high-dimensional no..:

Ding, Chen ; Dang, Chao ; Valdebenito, Marcos...
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2022

Link: https://zenodo.org/recor..

4

Inverse quantification of epistemic uncertainty under scarc..:

Faes, Matthias ; Broggi, Matteo ; Patelli, Edoardo...
13th International Conference on Applications of Statistics and Probability in Civil Engineering(ICASP13), Seoul, South Korea, May 26-30, 2019. , 2019

Link: http://hdl.handle.net/10..

5

A multivariate interval approach for inverse uncertainty qu..:

Faes, Matthias ; Broggi, Matteo ; Patelli, Edoardo...
https://strathprints.strath.ac.uk/71221/1/Faes_etal_MSSP_2019_A_Multivariate_interval_approach_for_inverse_uncertainty.pdf. , 2019

Link: https://strathprints.str..

6

Process Monitoring of Extrusion Based 3D Printing via Laser..:

Faes, Matthias ; Abbeloos, Wim ; Vogeler, Frederik...
http://arxiv.org/abs/1612.02219. , 2016

Link: http://arxiv.org/abs/161..

7

A self-learning Digital Twin for Process Control of fast pr..:

Dodt, Miriam B ; Persoons, Augustin ; Faes, Matthias G. R.
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2023

Link: https://zenodo.org/recor..

8

Accuracy of physics informed neural network used to model t..:

Damien Bonnet-Eymard ; Augustin Persoons ; Matthias Faes.
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2023

Link: https://zenodo.org/recor..

9

Bayesian parameter estimation of ligament properties based ..:

Bartsoen, Laura ; Faes, Matthias G.R ; Andersen, Michael Skipper...
https://vbn.aau.dk/da/publications/fbda2c4b-58c4-4db4-a94e-73908d57ea1a. , 2023

Link: https://vbn.aau.dk/da/pu..

10

Near-real-time online process control using grey-box models:

Dodt, Miriam B ; Persoons, Augustin ; Faes, Matthias G. R.
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2023

Link: https://zenodo.org/recor..

11

Robust design optimisation under lack-of-knowledge uncertai..:

van Mierlo, Conradus ; Persoons, Augustin ; Faes, Matthias G.R.
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.compstruc.2022.106910. , 2023

Link: https://hal.science/hal-..

12

Active learning in grey-box models for near-real-time onlin..:

Dodt, Miriam B ; Persoons, Augustin ; Faes, Matthias G.R.
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2022

Link: https://zenodo.org/recor..

13

Efficient engineering analysis with imprecise probabilities:

Beer, Michael ; Broggi, Matteo ; Faes, Matthias G.R...
https://www.repo.uni-hannover.de/handle/123456789/13314. , 2022

Link: https://www.repo.uni-han..

14

The sub-interval similarity : a general uncertainty quantif..:

Zhao, Yanlin ; Yang, Jianhong ; Faes, Matthias G.R..
https://strathprints.strath.ac.uk/80951/1/Zhao_etal_MSSP_The_sub_interval_similarity_a_general_uncertainty_quantification_metric.pdf. , 2022

Link: https://strathprints.str..

15

Active learning in grey-box models for near-real-time onlin..:

Dodt, Miriam B ; Persoons, Augustin ; Faes, Matthias G.R.
info:eu-repo/grantAgreement/EC/H2020/955393/. , 2022

Link: https://zenodo.org/recor..