7 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…
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…
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…