PRIN 2017 - 201752HKH8_004
Principal Investigator: Lorenzo Tamellini
Project Areas: Matematica Applicata
Year of start/end
2019 / 2025
PDGP CODE
DIT.AD021.092.001
Tag
Efficient and Acccurate Solvers | partial differential equations | Reduced Order Models
Activity
The objective of this research project is to design and analyze innovative numerical methods for the approximation of partial differential equations (PDEs) in computational sciences and engineering. The increasing complexity of realistic models and the evolution of the computational platforms and architectures are challenging the numerical analysis community to develop more efficient, effective, and innovative methods, able to incorporate uncertainty quantification, data analysis and high performance computing applications. Our research units share a consolidated expertise on advanced discretization schemes based on variational approaches, such as conforming and nonconforming finite elements (FEM), spectral and hp finite elements (hp-FEM), immersed methods, finite volumes (FV), as well as on reduced order methods (ROMs) that rely on the aforementioned schemes and on Uncertainty Quantification (UQ) methodologies.
Linked Products
- 2023, Journal article
Combining noisy well data and expert knowledge in a Bayesian calibration of a flow model under uncertainties: an application to solute transport in the Ticino basin
E.A. Baker, S.Manenti, A. Reali, G. Sangalli, L. Tamellini, and S. Todeschini - 2023, Journal article
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, and M. Diez - 2023, Journal article
Sparse-grids uncertainty quantification of part-scale additive manufacturing processes
M. Chiappetta, C. Piazzola, M. Carraturo, L. Tamellini, A. Reali, and F. Auricchio - 2023, Journal article
Uncertainty quantification in timber-like beams using sparse grids: Theory and examples with off-the-shelf software utilization
G. Balduzzi, F. Bonizzoni, and L. Tamellini - 2022, Journal article
Adaptive experimental design for multi-fidelity surrogate modeling of multi-disciplinary systems
J.D. Jakeman, S. Friedman, M.S. Eldred, L. Tamellini, A.A. Gorodetsky, and D. Allaire - 2022, Journal article
Combining the Morris method and multiple error metrics to assess aquifer characteristics and recharge in the lower Ticino Basin, in Italy
E.A. Baker, A. Cappato, A. Todeschini, L. Tamellini, G. Sangalli, A. Reali, and S. Manenti - 2022, Journal article
On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion
M. Eigel, O.G. Ernst, B. Sprungk, and L. Tamellini - 2021, Journal article
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
C. Piazzola, L. Tamellini, and R. Tempone - 2020, Journal article
Computational approaches for uncertainty quantification of naval engineering problems
R. Broglia, M. Diez, and L. Tamellini - 2021, Essay or book chapter
On expansions and nodes for sparse grid collocation of lognormal elliptic PDEs
O.G. Ernst, B. Sprungk, and L. Tamellini - 2021, Conference proceedings
Comparing multi-index stochastic collocation and radial basis function surrogates for ship resistance uncertainty quantification
C. Piazzola, L. Tamellini, R. Pellegrini, R. Broglia, A. Serani, and M. Diez - 2020, Conference proceedings
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, and M. Diez