About Me
Francesco Romor is a Postdoctoral Researcher at the Weierstrass Institute, specializing in numerical modelling and scientific machine learning. His research spans numerical analysis, scientific computing, computational fluid dynamics, optimal transport, uncertainty quantification, and reduced-order modelling. Combining mathematical modelling, high-performance computing, and machine learning, his work aims to develop efficient, data-driven solutions for complex scientific challenges.
Interests
- Numerical analysis of PDEs
- Scientific machine learning
- Model order reduction
- Uncertainty quantification
- High-performance computing
- Computational optimal transport
- Cardiovascular modelling
Education
PhD Mathematical analysis, modelling and applications
International School for Advanced Studies (SISSA)
MS Mathematics
University of Triest
BSc Mathematics
University of Triest
Preprints
Publications
(2025).
Friedrichs' systems discretized with the DGM: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity solutions.
Journal of Computational Physics.
(2025).
Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations.
Journal of Computational Physics.
(2023).
Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions.
Computer Methods in Applied Mechanics and Engineering.
(2023).
Non-linear Manifold Reduced-Order Models with Convolutional Autoencoders and Reduced Over-Collocation Method.
Journal of Scientific Computing.
(2022).
A Local Approach to Parameter Space Reduction for Regression and Classification Tasks.
Journal of Scientific Computing.