| Uncertainty quantification of a three-dimensional in-stent restenosis model with surrogate modelling D Ye, P Zun, V Krzhizhanovskaya, AG Hoekstra Journal of the Royal Society Interface 19 (187), 20210864, 2022 | 22 | 2022 |
| Non-intrusive and semi-intrusive uncertainty quantification of a multiscale in-stent restenosis model D Ye, A Nikishova, L Veen, P Zun, AG Hoekstra Reliability Engineering & System Safety 214, 107734, 2021 | 20 | 2021 |
| Uncertainty quantification patterns for multiscale models D Ye, L Veen, A Nikishova, J Lakhlili, W Edeling, OO Luk, ... Philosophical Transactions of the Royal Society A 379 (2197), 20200072, 2021 | 12 | 2021 |
| Tutorial applications for Verification, Validation and Uncertainty Quantification using VECMA toolkit D Suleimenova, H Arabnejad, WN Edeling, D Coster, OO Luk, J Lakhlili, ... Journal of Computational Science 53, 101402, 2021 | 9 | 2021 |
| Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots D Ye, V Krzhizhanovskaya, AG Hoekstra Journal of Computational Physics 497, 112639, 2024 | 5 | 2024 |
| Inverse uncertainty quantification of a mechanical model of arterial tissue with surrogate modelling S Kakhaia, P Zun, D Ye, V Krzhizhanovskaya Reliability Engineering & System Safety 238, 109393, 2023 | 4 | 2023 |
| Gaussian process learning of nonlinear dynamics D Ye, M Guo Communications in Nonlinear Science and Numerical Simulation 138, 108184, 2024 | 2 | 2024 |
| A parametric framework for kernel-based dynamic mode decomposition using deep learning K Kevopoulos, D Ye arXiv preprint arXiv:2409.16817, 2024 | | 2024 |
| Bayesian approach to Gaussian process regression with uncertain inputs D Ye, M Guo arXiv preprint arXiv:2305.11586, 2023 | | 2023 |
| Surrogate modelling and uncertainty quantification for multiscale simulation D Ye PhD Thesis, 2022 | | 2022 |
Alfons HoekstraVerified email at uva.nl
