Yearly Archives: 2024

144 posts

New MOX Report on “Multi-fidelity reduced-order surrogate modelling”

A new MOX Report entitled “Multi-fidelity reduced-order surrogate modelling” by Conti, P.; Guo, M.; Manzoni, A.; Frangi, A.; Brunton, S. L.; Kutz, J.N. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/83-2024.pdf Abstract: High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can […]

New MOX Report on “EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics”

A new MOX Report entitled “EKF-SINDy: Empowering the extended Kalman filter with sparse identification of nonlinear dynamics” by Rosafalco, L.; Conti, P.; Manzoni, A.; Mariani, S.; Frangi, A. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/82-2024.pdf Abstract: Measured data from a dynamical system can be assimilated […]

New MOX Report on “Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition”

A new MOX Report entitled “Error estimates for POD-DL-ROMs: a deep learning framework for reduced order modeling of nonlinear parametrized PDEs enhanced by proper orthogonal decomposition” by Brivio, S.; Franco, Nicola R.; Fresca, S.; Manzoni, A. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/85-2024.pdf Abstract: POD-DL-ROMs […]

New MOX Report on “On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields”

A new MOX Report entitled “On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields” by Franco, N.R.; Fraulin, D.; Manzoni, A.; Zunino, P. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/86-2024.pdf Abstract: Deep Learning is having a remarkable […]

New MOX Report on “Elucidating the cellular determinants of the end-systolic pressure-volume relationship of the heart via computational modelling”

A new MOX Report entitled “Elucidating the cellular determinants of the end-systolic pressure-volume relationship of the heart via computational modelling” by Regazzoni, F.; Poggesi, C.; Ferrantini, C. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/88-2024.pdf Abstract: The left ventricular end-systolic pressure-volume relationship (ESPVr) is a key […]

New MOX Report on “Modeling anisotropy and non-stationarity through physics-informed spatial regression”

A new MOX Report entitled “Modeling anisotropy and non-stationarity through physics-informed spatial regression” by Tomasetto, M.; Arnone, E.; Sangalli, L.M. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/90-2024.pdf Abstract: Many spatially dependent phenomena, that are of interest in environmental problems, are characterized by strong anisotropy and […]

New MOX Report on “Solving Semi-Linear Elliptic Optimal Control Problems with L1-Cost via Regularization and RAS-Preconditioned Newton Methods”

A new MOX Report entitled “Solving Semi-Linear Elliptic Optimal Control Problems with L1-Cost via Regularization and RAS-Preconditioned Newton Methods” by Ciaramella, G.; Kartmann, M.; Mueller, G. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/91-2024.pdf Abstract: We present a new parallel computational framework for the efficient solution […]

New MOX Report on “Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks”

A new MOX Report entitled “Deep Learning-based surrogate models for parametrized PDEs: handling geometric variability through graph neural networks” by Franco, N.R.; Fresca, S.; Tombari, F.; Manzoni, A. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/94-2024.pdf Abstract: Mesh-based simulations play a key role when modeling complex […]

New MOX Report on “VENI, VINDy, VICI – a variational reduced-order modeling framework with uncertainty quantification”

A new MOX Report entitled “VENI, VINDy, VICI – a variational reduced-order modeling framework with uncertainty quantification” by Conti, P.; Kneifl, J.; Manzoni, A.; Frangi, A.; Fehr, J.; Brunton, S.L.; Kutz, J.N. has appeared in the MOX Report Collection. Check it out here: https://www.mate.polimi.it/biblioteca/add/qmox/93-2024.pdf Abstract: The simulation of many complex […]