Davide Pradovera, PhD

Davide Pradovera, PhD

Post-doctoral researcher in Computational Math

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About me

Since February 2024, I have been a post-doctoral researcher at KTH in the section of Numerical Mathematics, with Prof. Sara Zahedi. My main interests here are the development and analysis of model order reduction algorithms for the surrogate modeling of parametric multiphase flow problems.

Before coming to Stockholm, I was a university assistant and post-doctoral researcher at the University of Vienna in the “Numerics of PDEs” group, with Prof. Ilaria Perugia.

I was awarded my PhD title in Mathematics at EPF Lausanne in December 2021. My PhD work in the “Scientific Computing and Uncertainty Quantification” (CSQI) group, under the supervision of Prof. Fabio Nobile, is summarized in my thesis, titled “Model order reduction based on functional rational approximants for parametric PDEs with meromorphic structure” (available here). Before that, I got my Master’s degree at EPF Lausanne, with a thesis on “Randomized low-rank approximation of matrices and tensors” (available here), under the supervision of Prof. Daniel Kressner.

I have also contributed to the organization of the model order reduction summer school 2020, a virtual event hosted at EPFL with the objective of disseminating knowledge on model order reduction.

As a teacher, my goal is empowering students to be active learners, able to discover the “truth” hidden behind the printed words on the textbook.

Research interests

My research interests lie at the intersection of:

  • model order reduction (non-intrusive surrogate modeling of parametric problems)

  • approximation theory (rational and non-smooth approximation)

  • numerical discretization of PDEs (finite elements, boundary elements, virtual elements)

  • wave-propagation applications (wave, Maxwell, and Helmholtz equation, scattering problems)

  • scientific computing (high-performance and parallel programming)

Being an applied computational mathematician, my research work so far has been characterized by a balance between theory (possibility of approximation, convergence, stability) and applications (optimal design problems in electrical and mechanical engineering, simulation of wave propagation). In all of this, I have strived for algorithmic efficiency and open-source coding: see here for the open-source MOR library RROMPy that I have developed during my PhD, and here and here for some of my open-source contributions on ZENODO and GitHub, respectively.