19 citations · 78 across the 13 of their papers we have counts for
14 papers
Quantum Fluctuations and Wigner Dynamics of a Carbon Nanotube Oscillator Driven by an Atomic Force Microscope
Conrad Silio, Cristian Staii
Suspended carbon nanotubes provide a useful platform for studying controlled nanomechanical motion in regimes where environmental fluctuations, memory effects, and quantum coherenc…
LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics
Sarah Katz, Francesco Romor, Jia-Jie Zhu +1
We introduce a novel conditional stochastic interpolant framework for generative modeling of three-dimensional shapes. The method builds on a recent LDDMM-based registration approa…
ROM for Viscous, Incompressible Flow in Polygons -- exponential -width bounds and convergence rate
Francesco Romor, Federico Pichi, Giovanni Stabile +2
We demonstrate exponential convergence of Reduced Order Model (ROM) approximations for mixed boundary value problems of the stationary, incompressible Navier-Stokes equations in pl…
Efficient Numerical Strategies for Entropy-Regularized Semi-Discrete Optimal Transport
Moaad Khamlich, Francesco Romor, Gianluigi Rozza
Semi-discrete optimal transport (SOT), which maps a continuous probability measure to a discrete one, is a fundamental problem with wide-ranging applications. Entropic regularizati…
Data assimilation performed with robust shape registration and graph neural networks: application to aortic coarctation
Francesco Romor, Felipe Galarce, Jan Brüning +2
Image-based, patient-specific modelling of hemodynamics can improve diagnostic capabilities and provide complementary insights to better understand the hemodynamic treatment outcom…
Generative Models for the Deformation of Industrial Shapes with Linear Geometric Constraints: model order and parameter space reductions
Guglielmo Padula, Francesco Romor, Giovanni Stabile +1
Real-world applications of computational fluid dynamics often involve the evaluation of quantities of interest for several distinct geometries that define the computational domain…
Explicable hyper-reduced order models on nonlinearly approximated solution manifolds of compressible and incompressible Navier-Stokes equations
Francesco Romor, Giovanni Stabile, Gianluigi Rozza
A slow decaying Kolmogorov n-width of the solution manifold of a parametric partial differential equation precludes the realization of efficient linear projection-based reduced-ord…
Friedrichs' systems discretized with the Discontinuous Galerkin method: domain decomposable model order reduction and Graph Neural Networks approximating vanishing viscosity solutions
Francesco Romor, Davide Torlo, Gianluigi Rozza
Friedrichs' systems (FS) are symmetric positive linear systems of first-order partial differential equations (PDEs), which provide a unified framework for describing various ellipt…
Non-linear manifold ROM with Convolutional Autoencoders and Reduced Over-Collocation method
Francesco Romor, Giovanni Stabile, Gianluigi Rozza
Non-affine parametric dependencies, nonlinearities and advection-dominated regimes of the model of interest can result in a slow Kolmogorov n-width decay, which precludes the reali…
Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering
Francesco Romor, Marco Tezzele, Markus Mrosek +2
Multi-fidelity models are of great importance due to their capability of fusing information coming from different numerical simulations, surrogates, and sensors. We focus on the ap…
A local approach to parameter space reduction for regression and classification tasks
Francesco Romor, Marco Tezzele, Gianluigi Rozza
Parameter space reduction has been proved to be a crucial tool to speed-up the execution of many numerical tasks such as optimization, inverse problems, sensitivity analysis, and s…
ATHENA: Advanced Techniques for High Dimensional Parameter Spaces to Enhance Numerical Analysis
Francesco Romor, Marco Tezzele, Gianluigi Rozza
ATHENA is an open source Python package for reduction in parameter space. It implements several advanced numerical analysis techniques such as Active Subspaces (AS), Kernel-based A…
Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces
Francesco Romor, Marco Tezzele, Gianluigi Rozza
Gaussian processes are employed for non-parametric regression in a Bayesian setting. They generalize linear regression, embedding the inputs in a latent manifold inside an infinite…
Kernel-based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method
Francesco Romor, Marco Tezzele, Andrea Lario +1
Nonlinear extensions to the active subspaces method have brought remarkable results for dimension reduction in the parameter space and response surface design. We further develop a…