Arun Kaintura
Arun Kaintura
K-Structura Consultancy
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Review of polynomial chaos-based methods for uncertainty quantification in modern integrated circuits
A Kaintura, T Dhaene, D Spina
Electronics 7 (3), 30, 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
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
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
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
Data-Efficient Machine Learning for Physics-Based Simulations
A Kaintura
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
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