| Machine learning methods in CFD for turbomachinery: A review J Hammond, N Pepper, F Montomoli, V Michelassi International Journal of Turbomachinery, Propulsion and Power 7 (2), 16, 2022 | 29 | 2022 |
| Adaptive learning for reliability analysis using support vector machines N Pepper, L Crespo, F Montomoli Reliability Engineering & System Safety 226, 108635, 2022 | 27 | 2022 |
| Multiscale uncertainty quantification with arbitrary polynomial chaos N Pepper, F Montomoli, S Sharma Computer Methods in Applied Mechanics and Engineering 357, 112571, 2019 | 23 | 2019 |
| Data fusion for uncertainty quantification with non-intrusive polynomial chaos N Pepper, F Montomoli, S Sharma Computer Methods in Applied Mechanics and Engineering 374, 113577, 2021 | 11 | 2021 |
| Meta-modeling on detailed geography for accurate prediction of invasive alien species dispersal N Pepper, L Gerardo-Giorda, F Montomoli Scientific Reports 9 (1), 16237, 2019 | 10 | 2019 |
| Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations R Ahlfeld, F Ciampoli, M Pietropaoli, N Pepper, F Montomoli Proceedings of the Institution of Mechanical Engineers, Part D: Journal of …, 2019 | 9 | 2019 |
| Probabilistic machine learning to improve generalisation of data-driven turbulence modelling J Ho, N Pepper, T Dodwell arXiv preprint arXiv:2301.09443, 2023 | 7* | 2023 |
| Local bi-fidelity field approximation with knowledge based neural networks for computational fluid dynamics N Pepper, A Gaymann, S Sharma, F Montomoli Scientific Reports 11 (1), 14459, 2021 | 7 | 2021 |
| Using remote sensing data within an optimal spatiotemporal model for invasive plant management: the case of Ailanthus altissima in the Alta Murgia National Park CM Baker, P Blonda, F Casella, F Diele, C Marangi, A Martiradonna, ... Scientific Reports 13 (1), 14587, 2023 | 3 | 2023 |
| A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators N Pepper, M Thomas, G De Ath, E Olivier, R Cannon, R Everson, ... Proceedings of the Royal Society A 479 (2271), 20220607, 2023 | 3 | 2023 |
| Identification of missing input distributions with an inverse multi-modal Polynomial Chaos approach based on scarce data N Pepper, F Montomoli, S Sharma Probabilistic Engineering Mechanics 65, 103138, 2021 | 3 | 2021 |
| Multi-fidelity uncertainty quantification of high Reynolds number turbulent flow around a rectangular 5: 1 cylinder M Sakuma, N Pepper, S Warnakulasuriya, F Montomoli, R Wuch-ner, ... Wind and Structures 34 (1), 127-136, 2022 | 1 | 2022 |
| Uncertainty quantification and missing data for turbomachinery with probabilistic equivalence and arbitrary polynomial chaos, applied to scroll compressors N Pepper, F Montomoli, F Giacomel, G Cavazzini, M Pinelli, N Casari, ... Turbo Expo: Power for Land, Sea, and Air 84225, V10BT28A007, 2020 | 1 | 2020 |
| Learning Generative Models for Climbing Aircraft from Radar Data N Pepper, M Thomas Journal of Aerospace Information Systems 21 (6), 474-481, 2024 | | 2024 |
| SeAr PC: Sensitivity Enhanced Arbitrary Polynomial Chaos N Pepper, F Montomoli, K Kantarakias arXiv preprint arXiv:2402.05507, 2024 | | 2024 |
| Context-Aware Generative Models for Prediction of Aircraft Ground Tracks N Pepper, G De Ath, M Thomas, R Everson, T Dodwell arXiv preprint arXiv:2309.14957, 2023 | | 2023 |
| A Non-Parametric Histogram Interpolation Method for Design Space Exploration N Pepper, F Montomoli, S Sharma Journal of Mechanical Design 144 (8), 081703, 2022 | | 2022 |
| MULTI-FIDELITY UNCERTAINTY QUANTIFICATION OF THE FLOW AROUND A RECTANGULAR 5: 1 CYLINDER M Sakuma, N Pepper, A Kodakkal, R Wüchner, KU Bletzinger, ... | | |
| IDENTIFYING INFORMATIVE FEATURES FOR DATA-DRIVEN TURBULENCE MODELLING J Ho, N Pepper, T Dodwell | | |
Prof Francesco MontomoliImperial College London, UK在 imperial.ac.uk 的电子邮件经过验证
Prof Francesco MontomoliImperial College London, UK在 imperial.ac.uk 的电子邮件经过验证
Luca Gerardo-GiordaJohannes Kepler University and RICAM, Linz在 jku.at 的电子邮件经过验证