Publications

VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
Generative models are transforming science and engineering by enabling efficient synthetization and exploration of new scenarios for …
Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz
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VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification
Progressive multi-fidelity learning for physical system predictions
Highly accurate datasets from numerical or physical experiments are often expensive and time-consuming to acquire, posing a significant …
Paolo Conti, Mengwu Guo, Attilio Frangi, Andrea Manzoni
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Progressive multi-fidelity learning for physical system predictions
Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
Inverse uncertainty quantification (UQ) tasks such as parameter estimation are computationally demanding whenever dealing with …
Filippo Zacchei, Paolo Conti, Attilio Frangi, Andrea Manzoni
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Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
Online learning in bifurcating dynamic systems via SINDy and Kalman filtering
We propose the use of the Extended Kalman Filter (EKF) for online data assimilation and update of a dynamic model, preliminary …
Luca Rosafalco, Paolo Conti, Andrea Manzoni, Stefano Mariani, Attilio Frangi
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Online learning in bifurcating dynamic systems via SINDy and Kalman filtering
EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics
Observed data from a dynamic system can be assimilated into a predictive model by means of Kalman filters. Nonlinear extensions of the …
Luca Rosafalco, Paolo Conti, Andrea Manzoni, Stefano Mariani, Attilio Frangi
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EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics
Soft sensor design using hierarchical multi-fidelity modeling with bayesian optimization for input variable selection
Soft sensors, or inferential sensors, are crucial in quality and process control systems because they allow for e!cient, online …
Johannes Lips, Hendrik Lens, Paolo Conti
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Soft sensor design using hierarchical multi-fidelity modeling with bayesian optimization for input variable selection
Multi-fidelity reduced-order surrogate modeling
High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly …
Paolo Conti, Mengwu Guo, Andrea Manzoni, Attilio Frangi, Steven L. Brunton, J. Nathan Kutz
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Multi-fidelity reduced-order surrogate modeling
Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Highly accurate simulations of complex phenomena governed by partial differential equations (PDEs) typically require intrusive methods …
Paolo Conti, Giorgio Gobat, Stefania Fresca, Andrea Manzoni, Attilio Frangi
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Reduced order modeling of parametrized systems through autoencoders and SINDy approach: continuation of periodic solutions
Multi-fidelity surrogate modeling using long short-term memory networks
When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between …
Paolo Conti, Mengwu Guo, Andrea Manzoni, Jan S. Hesthaven
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Multi-fidelity surrogate modeling using long short-term memory networks
Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities
Highly accurate numerical or physical experiments are often very time-consuming or expensive to obtain. When time or budget …
Mengwu Guo, Andrea Manzoni, Maurice Amendt, Paolo Conti, Jan S. Hesthaven
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Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities