| Nektar++: An open-source spectral/hp element framework CD Cantwell, D Moxey, A Comerford, A Bolis, G Rocco, G Mengaldo, ... Computer physics communications 192, 205-219, 2015 | 610 | 2015 |
| On the eddy-resolving capability of high-order discontinuous Galerkin approaches to implicit LES/under-resolved DNS of Euler turbulence RC Moura, G Mengaldo, J Peiró, SJ Sherwin Journal of Computational Physics 330, 615-623, 2017 | 153 | 2017 |
| Nektar++: Enhancing the capability and application of high-fidelity spectral/hp element methods D Moxey, CD Cantwell, Y Bao, A Cassinelli, G Castiglioni, S Chun, E Juda, ... Computer Physics Communications 249, 107110, 2020 | 123 | 2020 |
| A comparative study on polynomial dealiasing and split form discontinuous Galerkin schemes for under-resolved turbulence computations AR Winters, RC Moura, G Mengaldo, GJ Gassner, S Walch, J Peiro, ... Journal of Computational Physics 372, 1-21, 2018 | 120 | 2018 |
| Dealiasing techniques for high-order spectral element methods on regular and irregular grids G Mengaldo, D De Grazia, D Moxey, PE Vincent, SJ Sherwin Journal of Computational Physics 299, 56-81, 2015 | 118 | 2015 |
| Connections between the discontinuous Galerkin method and high‐order flux reconstruction schemes D De Grazia, G Mengaldo, D Moxey, PE Vincent, SJ Sherwin International Journal for Numerical Methods in Fluids 75 (12), 860-877, 2014 | 84 | 2014 |
| A Guide to the Implementation of Boundary Conditions in Compact High-Order Methods for Compressible Aerodynamics G Mengaldo, D De Grazia, J Peiro, A Farrington, F Witherden, PE Vincent, ... AIAA Aviation 2014, 2014 | 78 | 2014 |
| A concise guide to modelling the physics of embodied intelligence in soft robotics G Mengaldo, F Renda, SL Brunton, M Bächer, M Calisti, C Duriez, ... Nature Reviews Physics 4 (9), 595-610, 2022 | 67 | 2022 |
| Spatial eigensolution analysis of discontinuous Galerkin schemes with practical insights for under-resolved computations and implicit LES G Mengaldo, RC Moura, B Giralda, J Peiró, SJ Sherwin Computers & Fluids 169, 349-364, 2018 | 64 | 2018 |
| Current and emerging time-integration strategies in global numerical weather and climate prediction G Mengaldo, AA Wyszogrodzki, M Diamantakis, SJ Lock, FX Giraldo, ... | 62 | 2018 |
| On the connections between discontinuous Galerkin and flux reconstruction schemes: extension to curvilinear meshes G Mengaldo, D De Grazia, PE Vincent, SJ Sherwin Journal of Scientific Computing 67, 1272-1292, 2016 | 52 | 2016 |
| Non-modal analysis of spectral element methods: Towards accurate and robust large-eddy simulations P Fernandez, RC Moura, G Mengaldo, J Peraire Computer Methods in Applied Mechanics and Engineering 346, 43-62, 2019 | 46 | 2019 |
| Atlas: A library for numerical weather prediction and climate modelling W Deconinck, P Bauer, M Diamantakis, M Hamrud, C Kühnlein, P Maciel, ... Computer Physics Communications 220, 188-204, 2017 | 44 | 2017 |
| Spatial eigensolution analysis of energy-stable flux reconstruction schemes and influence of the numerical flux on accuracy and robustness. G Mengaldo, D De Grazia, RC Moura, SJ Sherwin J. Comput. Phys. 358 (1), 1-20, 2018 | 43 | 2018 |
| Industry-Relevant Implicit Large-Eddy Simulation of a High-Performance Road Car via Spectral/hp Element Methods G Mengaldo, D Moxey, M Turner, RC Moura, A Jassim, M Taylor, J Peiró, ... SIAM Review 63 (4), 2021 | 36 | 2021 |
| An LES setting for DG-based implicit LES with insights on dissipation and robustness RC Moura, G Mengaldo, J Peiró, SJ Sherwin Spectral and High Order Methods for Partial Differential Equations ICOSAHOM …, 2017 | 34 | 2017 |
| Spectral empirical orthogonal function analysis of weather and climate data OT Schmidt, G Mengaldo, G Balsamo, NP Wedi Monthly Weather Review 147 (8), 2979-2995, 2019 | 31 | 2019 |
| Evaluation of post-hoc interpretability methods in time-series classification H Turbé, M Bjelogrlic, C Lovis, G Mengaldo Nature Machine Intelligence 5 (3), 250-260, 2023 | 30 | 2023 |
| The ESCAPE project: energy-efficient scalable algorithms for weather prediction at exascale A Müller, W Deconinck, C Kühnlein, G Mengaldo, M Lange, N Wedi, ... Geoscientific Model Development 12 (10), 4425-4441, 2019 | 27 | 2019 |
| Efficient high-dimensional variational data assimilation with machine-learned reduced-order models R Maulik, V Rao, J Wang, G Mengaldo, E Constantinescu, B Lusch, ... Geoscientific Model Development 15, 3433–3445, 2022 | 24* | 2022 |
Spencer J. SherwinProfessor of Computational Fluid Mechanics, Imperial College London在 imperial.ac.uk 的电子邮件经过验证
