77 citations · 209 across the 17 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…