Luca Bertagna

Computational Science

Computational Science

(505) 844-6864

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185


I’m interested in numerical methods for PDEs, and their efficient implementation on current and future HPC architectures. My background is in applied mathematics, including PDEs, functional analysis, numerical analysis, and HPC. I am also interested in modern software design and testing, with the goal to enhance code readability, maintainability, and extensibility.


  • 2014: Ph.D., Applied Mathematics, Emory University (GA).
  • 2009: MS, Mathematical Engineering, Politecnico di Milano (Italy).
  • 2006: BS, Mathematical Engineering, Politecnico di Milano (Italy).


L.Bertagna, O.Guba, M.A.Taylor, J.G.Foucar, J.Larkin, A.M.Bradley, S.Rajamanickam, A.G.Salinger, (2020). A performance-portable nonhydrostatic atmospheric dycore for the Energy Exascale Earth System Model running at cloud-resolving resolutions. SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.

L.Bertagna, A.Quaini, L.G.Rebholz, A.Veneziani, (2019). On the sensitivity to the filtering radius in Leray models of incompressible flow. Contributions to Partial Differential Equations and Applications.

L.Bertagna, M.Deakin, O.Guba, D.Sunderland, A.M.Bradley, I.K.Tezaur, M.A.Taylor, A.G.Salinger, (2019). HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model. Geoscientific Model Development.

M.J.Hoffman, M.Perego, S.F.Price, W.H.Lipscomb, T.Zhang, D.Jacobsen, I.K.Tezaur, A.G.Salinger, R.Tuminaro, L.Bertagna, (2018). MPAS-Albany Land Ice (MALI): a variable-resolution ice sheet model for Earth system modeling using Voronoi grids. Geoscientific Model Development.

L.Bertagna, A.Quaini, A.Veneziani, (2016). Deconvolution‐based nonlinear filtering for incompressible flows at moderately large Reynolds numbers. International Journal for Numerical Methods in Fluids.

L.Bertagna, A.Veneziani, (2014). A model reduction approach for the variational estimation of vascular compliance by solving an inverse fluid–structure interaction problem. Inverse Problems.

L.Bertagna, M.D’Elia, M.Perego, A.Veneziani, (2014). Data assimilation in cardiovascular fluid–structure interaction problems: an introduction. Fluid-structure interaction and biomedical applications.