Deep operator learning-based surrogate models with uncertainty quantification for optimizing internal cooling channel rib profiles I Sahin, C Moya, A Mollaali, G Lin, G Paniagua International Journal of Heat and Mass Transfer 219, 124813, 2024 | 7 | 2024 |
Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks C Moya, A Mollaali, Z Zhang, L Lu, G Lin arXiv preprint arXiv:2402.15406, 2024 | 2 | 2024 |
A physics-guided bi-fidelity fourier-featured operator learning framework for predicting time evolution of drag and lift coefficients A Mollaali, I Sahin, I Raza, C Moya, G Paniagua, G Lin Fluids 8 (12), 323, 2023 | 1 | 2023 |
Investigation on the effects of measurement and temporal uncertainties on rolling element bearings prognostics M Behzad, A Mollaali, M Mirfarah, HA Arghand Journal of Theoretical and Applied Vibration and Acoustics 6 (1), 1-16, 2020 | 1 | 2020 |
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions Z Kong, A Mollaali, C Moya, N Lu, G Lin arXiv preprint arXiv:2311.16519, 2023 | | 2023 |
A New Methodology to Deal with the Multi-phase Degradation in Rolling Element Bearing Prognostics A Mollaali, M Behzad, M Mirfarah Advances in Asset Management and Condition Monitoring: COMADEM 2019, 855-869, 2020 | | 2020 |