Publications
Discover our latest research
Gualandi, A., Dal Zilio, L., Faranda, D. and Mengaldo, G., 2024. Similarities and differences between natural and simulated slow earthquakes (No. EGU24-20926). Copernicus Meetings. Read
Ong, K., van der Heever, W., Satapathy, R., Cambria, E. and Mengaldo, G., 2023, December. FinXABSA: Explainable finance through aspect-based sentiment analysis. In 2023 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 773-782). IEEE. Read
Tonicello, N., Moura, R.C., Lodato, G. and Mengaldo, G., 2023. Fully-discrete spatial eigenanalysis of discontinuous spectral element methods: insights into well-resolved and under-resolved vortical flows. Computers & Fluids, 266, p.106060. Read
Tan, Y.J., Mengaldo, G. and Laschi, C., 2023. Artificial Muscles for Underwater Soft Robots: Materials and Their Interactions. Annual Review of Condensed Matter Physics, 15. Read
Lin, S., Mengaldo, G. and Maulik, R., 2023. Online data-driven changepoint detection for high-dimensional dynamical systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 33(10). Read
Rogowski, M., Yeung, B.C., Schmidt, O.T., Maulik, R., Dalcin, L., Parsani, M. and Mengaldo, G., 2023. Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package. arXiv preprint arXiv:2309.11808. Read
Yeo, W.J., van der Heever, W., Mao, R., Cambria, E., Satapathy, R. and Mengaldo, G., 2023. A comprehensive review on financial explainable AI. arXiv preprint arXiv:2309.11960. Read
Duong, C., Raghuram, V.C., Lee, A., Mao, R., Mengaldo, G. and Cambria, E., 2023. Neurosymbolic AI for mining public opinions about wildfires. Cognitive Computation, pp.1-23. Read
Turbé, H., Bjelogrlic, M., Lovis, C. and Mengaldo, G., 2023. Evaluation of post-hoc interpretability methods in time-series classification. Nature Machine Intelligence, 5(3), pp.250-260. Read
Gualandi, A., Faranda, D., Marone, C., Cocco, M. and Mengaldo, G., 2023. Deterministic and stochastic chaos characterize laboratory earthquakes. Earth and Planetary Science Letters, 604, p.117995. Read
Lario, A., Maulik, R., Schmidt, O.T., Rozza, G. and Mengaldo, G., 2022. Neural-network learning of SPOD latent dynamics. Journal of Computational Physics, 468, p.111475. Read
Maulik, R., Rao, V., Wang, J., Mengaldo, G., Constantinescu, E., Lusch, B., Balaprakash, P., Foster, I. and Kotamarthi, R., 2022. AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reduced-order models. Geoscientific Model Development Discussions, 2022, pp.1-20. Read
Hess, M.W., Lario, A., Mengaldo, G. and Rozza, G., 2022, November. Reduced order modeling for spectral element methods: current developments in Nektar++ and further perspectives. In Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2020+ 1: Selected Papers from the ICOSAHOM Conference, Vienna, Austria, July 12-16, 2021 (pp. 361-374). Cham: Springer International Publishing. Read
Mengaldo, G., Renda, F., Brunton, S.L., Bächer, M., Calisti, M., Duriez, C., Chirikjian, G.S. and Laschi, C., 2022. A concise guide to modelling the physics of embodied intelligence in soft robotics. Nature Reviews Physics, 4(9), pp.595-610. Read
Tonicello, N., Lario, A., Rozza, G. and Mengaldo, G., 2022. Non-intrusive reduced order models for the accurate prediction of bifurcating phenomena in compressible fluid dynamics. arXiv preprint arXiv:2212.10198. Read
Moura, R.C., Fernandes, L.D., Silva, A.F.C., Mengaldo, G. and Sherwin, S.J., 2022. Spectral/hp element methods' linear mechanism of (apparent) energy transfer in Fourier space: Insights into dispersion analysis for implicit LES. Journal of Computational Physics, 471, p.111613. Read
Mengaldo, G. and Maulik, R., 2021. Pyspod: A python package for spectral proper orthogonal decomposition (spod). Journal of Open Source Software, 6(60), p.2862. Read
Maulik, R. and Mengaldo, G., 2021, November. PyParSVD: A streaming, distributed and randomized singular-value-decomposition library. In 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7) (pp. 19-25). IEEE. Read
Mengaldo, G., Moxey, D., Turner, M., Moura, R.C., Jassim, A., Taylor, M., Peiro, J. and Sherwin, S., 2021. Industry-relevant implicit large-eddy simulation of a high-performance road car via spectral/hp element methods. SIAM Review, 63(4), pp.723-755. Read
Moxey, D., Cantwell, C.D., Bao, Y., Cassinelli, A., Castiglioni, G., Chun, S., Juda, E., Kazemi, E., Lackhove, K., Marcon, J. and Mengaldo, G., 2020. Nektar++: Enhancing the capability and application of high-fidelity spectral/hp element methods. Computer Physics Communications, 249, p.107110. Read
Dorschner, B., Yu, K., Mengaldo, G. and Colonius, T., 2020. A fast multi-resolution lattice Green's function method for elliptic difference equations. Journal of Computational Physics, 407, p.109270. Read
Moura, R.C., Fernandez, P., Mengaldo, G. and Sherwin, S.J., 2020. Viscous diffusion effects in the eigenanalysis of (hybridisable) DG methods. In Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2018: Selected Papers from the ICOSAHOM Conference, London, UK, July 9-13, 2018 (pp. 371-382). Springer International Publishing. Read
Fernandez, P., Moura, R.C., Mengaldo, G. and Peraire, J., 2019. Non-modal analysis of spectral element methods: Towards accurate and robust large-eddy simulations. Computer Methods in Applied Mechanics and Engineering, 346, pp.43-62. Read
Schmidt, O.T., Mengaldo, G., Balsamo, G. and Wedi, N.P., 2019. Spectral empirical orthogonal function analysis of weather and climate data. Monthly Weather Review, 147(8), pp.2979-2995. Read
Mengaldo, G., 2019. Batch 1: definition of several weather & climate dwarfs. arXiv preprint arXiv:1908.06089. Read
Müller, A., Deconinck, W., Kühnlein, C., Mengaldo, G., Lange, M., Wedi, N., Bauer, P., Smolarkiewicz, P.K., Diamantakis, M., Lock, S.J. and Hamrud, M., 2019. The ESCAPE project: energy-efficient scalable algorithms for weather prediction at exascale. Geoscientific Model Development, 12(10), pp.4425-4441. Read
Mengaldo, G., Moura, R.C., Giralda, B., Peiró, J. and Sherwin, S.J., 2018. Spatial eigensolution analysis of discontinuous Galerkin schemes with practical insights for under-resolved computations and implicit LES. Computers & Fluids, 169, pp.349-364. Read
Winters, A.R., Moura, R.C., Mengaldo, G., Gassner, G.J., Walch, S., Peiro, J. and Sherwin, S.J., 2018. A comparative study on polynomial dealiasing and split form discontinuous Galerkin schemes for under-resolved turbulence computations. Journal of Computational Physics, 372, pp.1-21. Read
Mengaldo, G., Wyszogrodzki, A., Diamantakis, M., Lock, S.J., Giraldo, F.X. and Wedi, N.P., 2019. Current and emerging time-integration strategies in global numerical weather and climate prediction. Archives of Computational Methods in Engineering, 26, pp.663-684. Read
Mengaldo, G., De Grazia, D., Moura, R.C. and Sherwin, S.J., 2018. Spatial eigensolution analysis of energy-stable flux reconstruction schemes and influence of the numerical flux on accuracy and robustness. J. Comput. Phys., 358(1), pp.1-20. Read
Moura, R.C., Mengaldo, G., Peiró, J. and Sherwin, S.J., 2017. On the eddy-resolving capability of high-order discontinuous Galerkin approaches to implicit LES/under-resolved DNS of Euler turbulence. Journal of Computational Physics, 330, pp.615-623. Read
Moura, R.C., Mengaldo, G., Peiró, J. and Sherwin, S.J., 2017. An LES setting for DG-based implicit LES with insights on dissipation and robustness. In Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016: Selected Papers from the ICOSAHOM conference, June 27-July 1, 2016, Rio de Janeiro, Brazil (pp. 161-173). Springer International Publishing. Read
Mengaldo, G., Liska, S., Yu, K., Colonius, T. and Jardin, T., 2017. Immersed boundary lattice Green function methods for external aerodynamics. In 23rd AIAA computational fluid dynamics conference (p. 3621). Read
Deconinck, W., Bauer, P., Diamantakis, M., Hamrud, M., Kühnlein, C., Maciel, P., Mengaldo, G., Quintino, T., Raoult, B., Smolarkiewicz, P.K. and Wedi, N.P., 2017. Atlas: A library for numerical weather prediction and climate modelling. Computer Physics Communications, 220, pp.188-204. Read
Moxey, D., Cantwell, C.D., Mengaldo, G., Serson, D., Ekelschot, D., Peiró, J., Sherwin, S.J. and Kirby, R.M., 2017. Towards p-adaptive spectral/hp element methods for modelling industrial flows. In Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2016: Selected Papers from the ICOSAHOM conference, June 27-July 1, 2016, Rio de Janeiro, Brazil (pp. 63-79). Springer International Publishing. Read
Moura, R., Fernandez, P. and Mengaldo, G., 2017, November. Diffusion and dispersion characteristics of hybridized discontinuous Galerkin methods for under-resolved turbulence simulations. In APS Division of Fluid Dynamics Meeting Abstracts (pp. F31-007). Read
Mengaldo, G., De Grazia, D., Vincent, P.E. and Sherwin, S.J., 2016. On the connections between discontinuous Galerkin and flux reconstruction schemes: extension to curvilinear meshes. Journal of Scientific Computing, 67, pp.1272-1292. Read
Cantwell, C.D., Moxey, D., Comerford, A., Bolis, A., Rocco, G., Mengaldo, G., De Grazia, D., Yakovlev, S., Lombard, J.E., Ekelschot, D. and Jordi, B., 2015. Nektar++: An open-source spectral/hp element framework. Computer physics communications, 192, pp.205-219. Read
Mengaldo, G., Kravtsova, M., Ruban, A.I. and Sherwin, S.J., 2015. Triple-deck and direct numerical simulation analyses of high-speed subsonic flows past a roughness element. Journal of Fluid Mechanics, 774, pp.311-323. Read
Mengaldo, G., De Grazia, D., Moxey, D., Vincent, P.E. and Sherwin, S.J., 2015. Dealiasing techniques for high-order spectral element methods on regular and irregular grids. Journal of Computational Physics, 299, pp.56-81. Read
De Grazia, D., Mengaldo, G., Moxey, D., Vincent, P.E. and Sherwin, S., 2014. Connections between the discontinuous Galerkin method and high‐order flux reconstruction schemes. International journal for numerical methods in fluids, 75(12), pp.860-877. Read
Mengaldo, G., De Grazia, D., Witherden, F., Farrington, A., Vincent, P., Sherwin, S. and Peiro, J., 2014. A guide to the implementation of boundary conditions in compact high-order methods for compressible aerodynamics. In 7th AIAA Theoretical Fluid Mechanics Conference (p. 2923). Read
Mengaldo, G., 2015. Discontinuous spectral/hp element methods: development, analysis and applications to compressible flows (Doctoral dissertation, Imperial College London). Read
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