77 citations · 209 across the 17 of their papers we have counts for
18 papers
Multi-Fidelity SINDy: Sparse Discovery of Nonlinear Dynamical Systems with Fidelity-Weighted Measurements
Filippo Zacchei, Ana Larrañaga, Attilio Frangi +2
Data from simulations and experiments are rarely noise-free and often exhibit heterogeneous levels of fidelity. Measurement uncertainty may vary across repeated observations, sensi…
One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators
Teng Ma, Luca Rosafalco, Wei Cui +2
Extrapolative prediction of complex nonlinear dynamics remains a central challenge in engineering. This study proposes a one-shot learning method to identify global frequency-respo…
Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
Filippo Zacchei, Paolo Conti, Attilio Alberto Frangi +1
Inverse uncertainty quantification (UQ) tasks such as parameter estimation are computationally demanding whenever dealing with physics-based models, and typically require repeated…
Toward Enhanced Inertial Sensing via Dynamically Soft Topological States in Piezoelectric Microacoustic Metamaterials
Onurcan Kaya, Niccolo Scalise Pantuso, Marco Galli +10
In recent decades, microelectromechanical systems (MEMS)-based gyroscopes have been employed to meet positioning and navigation demands of a plethora of commercially available devi…
Reduced order modelling of Hopf bifurcations for the Navier-Stokes equations through invariant manifolds
Alessio Colombo, Alessandra Vizzaccaro, Cyril Touzé +3
This work introduces a parametric simulation-free reduced order model for incompressible flows undergoing a Hopf bifurcation, leveraging the parametrisation method for invariant ma…
Progressive multi-fidelity learning with neural networks for physical system predictions
Paolo Conti, Mengwu Guo, Attilio Frangi +1
Highly accurate datasets from numerical or physical experiments are often expensive and time-consuming to acquire, posing a significant challenge for applications that require prec…
Reduced-order modelling of parameter-dependent systems with invariant manifolds: application to Hopf bifurcations in follower force problems
André de F. Stabile, Alessandra Vizzaccaro, Loïc Salles +3
The direct parametrisation method for invariant manifolds is adjusted to consider a varying parameter. More specifically, the case of systems experiencing a Hopf bifurcation in the…
Online learning in bifurcating dynamic systems via SINDy and Kalman filtering
Luca Rosafalco, Paolo Conti, Andrea Manzoni +2
We propose the use of the Extended Kalman Filter (EKF) for online data assimilation and update of a dynamic model, preliminary identified through the Sparse Identification of Nonli…
VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
Paolo Conti, Jonas Kneifl, Andrea Manzoni +4
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent thi…
EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics
Luca Rosafalco, Paolo Conti, Andrea Manzoni +2
Measured data from a dynamical system can be assimilated into a predictive model by means of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended Kalman…
Reduced order modelling of fully coupled electro-mechanical systems through invariant manifolds with applications to microstructures
Attilio Frangi, Alessio Colombo, Alessandra Vizzaccaro +1
This paper presents the first application of the direct parametrisation method for invariant manifolds to a fully coupled multiphysics problem involving the nonlinear vibrations of…
Multi-fidelity reduced-order surrogate modeling
Paolo Conti, Mengwu Guo, Andrea Manzoni +3
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations…
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…
Reduced order modeling of nonlinear microstructures through Proper Orthogonal Decomposition
Gobat G., Opreni A., Fresca S. +2
We apply the Proper Orthogonal Decomposition (POD) method for the efficient simulation of several scenarios undergone by Micro-Electro-Mechanical-Systems, involving nonlinearites o…