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Chiara Piazzola
Chiara Piazzola
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Title
Cited by
Cited by
Year
A low-rank projector-splitting integrator for the Vlasov–Maxwell equations with divergence correction
L Einkemmer, A Ostermann, C Piazzola
Journal of Computational Physics 403, 109063, 2020
432020
Numerical low-rank approximation of matrix differential equations
H Mena, A Ostermann, LM Pfurtscheller, C Piazzola
Journal of Computational and Applied Mathematics 340, 602-614, 2018
402018
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
C Piazzola, L Tamellini, R Tempone
Mathematical Biosciences 332, 108514, 2021
272021
Convergence of a low-rank Lie--Trotter splitting for stiff matrix differential equations
A Ostermann, C Piazzola, H Walach
SIAM Journal on Numerical Analysis 57 (4), 1947-1966, 2019
272019
Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance
C Piazzola, L Tamellini, R Pellegrini, R Broglia, A Serani, M Diez
Engineering with Computers 39 (3), 2209-2237, 2023
182023
A splitting approach for the magnetic Schrödinger equation
M Caliari, A Ostermann, C Piazzola
Journal of Computational and Applied Mathematics 316, 74-85, 2017
172017
The Sparse Grids Matlab kit--a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
C Piazzola, L Tamellini
arXiv preprint arXiv:2203.09314, 2022
162022
Uncertainty quantification of ship resistance via multi-index stochastic collocation and radial basis function surrogates: A comparison
C Piazzola, L Tamellini, R Pellegrini, R Broglia, A Serani, M Diez
AIAA Aviation 2020 Forum, 3160, 2020
152020
Algorithm X: The Sparse Grids Matlab Kit-a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
C Piazzola, L Tamellini
ACM Transactions on Mathematical Software, 2023
82023
Sparse-grids uncertainty quantification of part-scale additive manufacturing processes
M Chiappetta, C Piazzola, M Carraturo, L Tamellini, A Reali, F Auricchio
International Journal of Mechanical Sciences 256, 108476, 2023
62023
Inverse and forward sparse-grids-based uncertainty quantification analysis of laser-based powder bed fusion of metals.
M Chiappetta, C Piazzola, M Carraturo, L Tamellini, A Reali, F Auricchio
CoRR, 2022
12022
Uncertainty quantification analysis of bifurcations of the Allen--Cahn equation with random coefficients
C Kuehn, C Piazzola, E Ullmann
arXiv preprint arXiv:2404.04639, 2024
2024
Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing
M Chiappetta, C Piazzola, L Tamellini, A Reali, F Auricchio, M Carraturo
arXiv preprint arXiv:2311.03823, 2023
2023
Surrogate-based Bayesian characterization of porous and deformable aquifer systems in water stressed regions
Y Li, C Zoccarato, L Tamellini, C Piazzola, P Ezquerro, G Bru, ...
EGU General Assembly Conference Abstracts, EGU-8761, 2023
2023
Surrogate-based Bayesian characterization of porous and deformable aquifer systems in water stressed regions
L Yueting, C Zoccarato, L Tamellini, C Piazzola, P Ezquerro, G Bru, ...
EGU General Assembly 2023, 2023
2023
Uncertainty quantification and identifiability of SIR-like dynamical systems
C Piazzola
2022
Dynamical low-rank approaches for differential equations
C Piazzola
University of Innsbruck, 2019
2019
Application of Multi-Fidelity Surrogate Models to Metal Additive Manufacturing
M Chiappetta, C Piazzola, M Carraturo, L Tamellini, A Reali, F Auricchio
YIC2023–7th ECCOMAS Young Investigators Conference (Porto, 19-21 Giugno …, 0
A splitting approach for the KP and the magnetic Schrödinger equation
A Ostermann, M Caliari, C Piazzola
MS06-Enabling technologies for uncertainty quantification and opti-mization in real-world applications
C Piazzola, R Pellegrini
Local organizing committee, 50, 0
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Articles 1–20