| Review of polynomial chaos-based methods for uncertainty quantification in modern integrated circuits A Kaintura, T Dhaene, D Spina Electronics 7 (3), 30, 2018 | 139 | 2018 |
| Measurement uncertainty propagation in transistor model parameters via polynomial chaos expansion A Petrocchi, A Kaintura, G Avolio, D Spina, T Dhaene, A Raffo, ... IEEE Microwave and Wireless Components Letters 27 (6), 572-574, 2017 | 56 | 2017 |
| A Kriging and Stochastic Collocation ensemble for uncertainty quantification in engineering applications A Kaintura, D Spina, I Couckuyt, L Knockaert, W Bogaerts, T Dhaene Engineering with Computers 33, 935-949, 2017 | 16 | 2017 |
| Machine learning for fast characterization of magnetic logic devices A Kaintura, K Foss, I Couckuyt, T Dhaene, O Zografos, A Vaysset, B Sorée 2018 IEEE electrical design of advanced packaging and systems symposium …, 2018 | 10 | 2018 |
| Fast characterization of input-output behavior of non-charge-based logic devices by machine learning A Kaintura, K Foss, O Zografos, I Couckuyt, A Vaysset, T Dhaene, B Sorée Electronics 9 (9), 1381, 2020 | | 2020 |
| Data-Efficient Machine Learning for Physics-Based Simulations A Kaintura | | 2019 |
| Comparison study of PC and kriging based surrogate modeling A Kaintura, D Spina, I Couckuyt, T Dhaene ECCOMAS, Conference on CFD and Optimization, 1-1, 2016 | | 2016 |