77 citations · 118 across the 5 of their papers we have counts for
5 papers
Nonlinear model order reduction of resonant piezoelectric micro-actuators: an invariant manifold approach
Andrea Opreni, Giorgio Gobat, Cyril Touzé +1
This paper presents a novel derivation of the direct parametrisation method for invariant manifolds able to build simulation-free reduced-order models for nonlinear piezoelectric s…
Direct parametrisation of invariant manifolds for non-autonomous forced systems including superharmonic resonances
Alessandra Vizzaccaro, Giorgio Gobat, Attilio Frangi +1
The direct parametrisation method for invariant manifold is a model-order reduction technique that can be applied to nonlinear systems described by PDEs and discretised e.g. with a…
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Paolo Conti, Giorgio Gobat, Stefania Fresca +2
Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods and entail expensive computational costs, whi…
Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches
Giorgio Gobat, Stefania Fresca, Andrea Manzoni +1
Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless app…
Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures
Stefania Fresca, Giorgio Gobat, Patrick Fedeli +2
We propose a non-intrusive Deep Learning-based Reduced Order Model (DL-ROM) capable of capturing the complex dynamics of mechanical systems showing inertia and geometric nonlineari…