91 citations · 216 across the 9 of their papers we have counts for
9 papers
Reliability of Probabilistic Emulation of Physical Systems
Sam F. Greenbury, Radka Jersakova, Paolo Conti +5
Two dominant approaches have emerged for generating probabilistic forecasts of physical systems: generative models, such as diffusion or flow matching; and ensembles of determinist…
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…
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…
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…
Multi-fidelity surrogate modeling using long short-term memory networks
Paolo Conti, Mengwu Guo, Andrea Manzoni +1
When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for para…