Lorenzo Tamellini
Primo Ricercatore
Researcher at CNR-IMATI since February 2016. More information can be found at my personal page
Research Activity
- Uncertainty Quantification
- Isogeometric Analysis
- Additive Manufacturing
- Computational fluid dynamics
- Computational geophysics.
Projects
FrontUQ18
HUTCHINSON
MICHELIN 2015-2017
PRIN 2017 - 201752HKH8_004
TOTAL S.A.
CAxMan
CAxMan PV
Publications
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
Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes within an aquifer. How...
CNR@People | DOI: 10.1007/s13137-023-00219-8
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
This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncer...
CNR@People | DOI: 10.1007/s00366-021-01588-0
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
The present paper aims at applying uncertainty quantification methodologies to process simulations of powder bed fusion of metal. In particular, for a part-scale thermomechanical model of an Inconel 6...
CNR@People | DOI: 10.1016/j.ijmecsci.2023.108476
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
When dealing with timber structures, the characteristic strength and stiffness of the material are made highly variable and uncertain by the unavoidable, yet hardly predictable, presence of knots and ...
CNR@People | DOI: 10.1016/j.conbuildmat.2023.132730
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
We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce "coupling" variables wit...
CNR@People | DOI: 10.1002/nme.6958
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
Groundwater flow model accuracy is often limited by the uncertainty in model parameters that characterize aquifer properties and aquifer recharge. Aquifer properties such as hydraulic conductivity can...
CNR@People | DOI: 10.1016/j.jhydrol.2022.128536
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
Convergence of an adaptive collocation method for the parametric stationary diffusion equation with finite-dimensional affine coefficient is shown. The adaptive algorithm relies on a recently introduc...
CNR@People | DOI: 10.1137/20M1364722
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
We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus i...
CNR@People | DOI: 10.1016/j.mbs.2020.108514
2020, Journal article
Compressive isogeometric analysis
S. Brugiapaglia, L. Tamellini, and M. Tani
This work is motivated by the difficulty in assembling the Galerkin matrix when solving Partial Differential Equations (PDEs) with Isogeometric Analysis (IGA) using B-splines of moderate-to-high polyn...
CNR@People | DOI: 10.1016/j.camwa.2020.11.004
2020, Journal article
Computational approaches for uncertainty quantification of naval engineering problems
R. Broglia, M. Diez, and L. Tamellini
The design of efficient seagoing vessels is key to a sustainable blue growth. Computer simulations are routinely used to explore different designs, but a reliable analysis must take into account the u...
CNR@People | Link
2020, Journal article
Parametric shape optimization for combined additive-subtractive manufacturing
L. Tamellini, M.Chiumenti, C. Altenhofen, M. Attene, O. Barrowclough, M. Livesu, F. Marini, M. Martinelli, and V. Skytt V.
In industrial practice, additive manufacturing (AM) processes are often followed by post-processing operations such as heat treatment, subtractive machining, milling, etc., to achieve the desired surf...
CNR@People | DOI: 10.1007/s11837-019-03886-x
2019, Journal article
IGA-based multi-index stochastic collocation for random PDEs on arbitrary domains
J. Beck, L. Tamellini, and R. Tempone
This paper proposes an extension of the Multi-Index Stochastic Collocation (MISC) method for forward uncertainty quantification (UQ) problems in computational domains of shape other than a square or c...
CNR@People | DOI: 10.1016/j.cma.2019.03.042
2018, Journal article
A sparse-grid isogeometric solver
J. Beck, G. Sangalli, and L. Tamellini
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can ben...
CNR@People | DOI: 10.1016/j.cma.2018.02.017
2018, Journal article
Convergence of sparse collocation for functions of countably many Gaussian random variables (with application to elliptic PDEs)
O.G. Ernst, B. Sprungk, and L. Tamellini
We give a convergence proof for the approximation by sparse collocation of Hilbert-space-valued functions depending on countably many Gaussian random variables. Such functions appear as solutions of e...
CNR@People | DOI: 10.1137/17M1123079
2018, Journal article
Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins
I. Colombo, F. Nobile, G. Porta, A. Scotti, and L. Tamellini
In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, n...
CNR@People | DOI: 10.1016/j.cma.2017.08.049
2017, Journal article
Optimal-order isogeometric collocation at Galerkin superconvergent points
M. Montardini, G. Sangalli, and L. Tamellini
In this paper we investigate numerically the order of convergence of an isogeometric collocation method that builds upon the least-squares collocation method presented in Anitescu et al. (2015) and th...
CNR@People | DOI: 10.1016/j.cma.2016.09.043
2016, Journal article
Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs
F. Nobile, L. Tamellini, and R. Tempone
In this work we provide a convergence analysis for the quasi-optimal version of the sparse-grids stochastic collocation method we presented in a previous work: "On the optimal polynomial approxim...
CNR@People | DOI: 10.1007/s00211-015-0773-y
2016, Journal article
Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity
A.-L. Haji-Ali, F. Nobile, L. Tamellini, and R. Tempone
We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient ...
CNR@People | DOI: 10.1007/s10208-016-9327-7
2016, Journal article
Multi-Index Stochastic Collocation for random PDEs
A.-L. Haji-Ali, F. Nobile, L. Tamellini, and R. Tempone
In this work we introduce the Multi-Index Stochastic Collocation method (MISC) for computing statistics of the solution of a PDE with random data. MISC is a combination technique based on mixed differ...
CNR@People | DOI: 10.1016/j.cma.2016.03.029
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
Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes within an aquifer. How...
CNR@People | DOI: 10.1007/s13137-023-00219-8
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
This paper presents a comparison of two multi-fidelity methods for the forward uncertainty quantification of a naval engineering problem. Specifically, we consider the problem of quantifying the uncer...
CNR@People | DOI: 10.1007/s00366-021-01588-0
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
The present paper aims at applying uncertainty quantification methodologies to process simulations of powder bed fusion of metal. In particular, for a part-scale thermomechanical model of an Inconel 6...
CNR@People | DOI: 10.1016/j.ijmecsci.2023.108476
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
When dealing with timber structures, the characteristic strength and stiffness of the material are made highly variable and uncertain by the unavoidable, yet hardly predictable, presence of knots and ...
CNR@People | DOI: 10.1016/j.conbuildmat.2023.132730
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
We present an adaptive algorithm for constructing surrogate models of multi-disciplinary systems composed of a set of coupled components. With this goal we introduce "coupling" variables wit...
CNR@People | DOI: 10.1002/nme.6958
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
Groundwater flow model accuracy is often limited by the uncertainty in model parameters that characterize aquifer properties and aquifer recharge. Aquifer properties such as hydraulic conductivity can...
CNR@People | DOI: 10.1016/j.jhydrol.2022.128536
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
Convergence of an adaptive collocation method for the parametric stationary diffusion equation with finite-dimensional affine coefficient is shown. The adaptive algorithm relies on a recently introduc...
CNR@People | DOI: 10.1137/20M1364722
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
We provide an overview of the methods that can be used for prediction under uncertainty and data fitting of dynamical systems, and of the fundamental challenges that arise in this context. The focus i...
CNR@People | DOI: 10.1016/j.mbs.2020.108514
2020, Journal article
Compressive isogeometric analysis
S. Brugiapaglia, L. Tamellini, and M. Tani
This work is motivated by the difficulty in assembling the Galerkin matrix when solving Partial Differential Equations (PDEs) with Isogeometric Analysis (IGA) using B-splines of moderate-to-high polyn...
CNR@People | DOI: 10.1016/j.camwa.2020.11.004
2020, Journal article
Computational approaches for uncertainty quantification of naval engineering problems
R. Broglia, M. Diez, and L. Tamellini
The design of efficient seagoing vessels is key to a sustainable blue growth. Computer simulations are routinely used to explore different designs, but a reliable analysis must take into account the u...
CNR@People | Link
2020, Journal article
Parametric shape optimization for combined additive-subtractive manufacturing
L. Tamellini, M.Chiumenti, C. Altenhofen, M. Attene, O. Barrowclough, M. Livesu, F. Marini, M. Martinelli, and V. Skytt V.
In industrial practice, additive manufacturing (AM) processes are often followed by post-processing operations such as heat treatment, subtractive machining, milling, etc., to achieve the desired surf...
CNR@People | DOI: 10.1007/s11837-019-03886-x
2019, Journal article
IGA-based multi-index stochastic collocation for random PDEs on arbitrary domains
J. Beck, L. Tamellini, and R. Tempone
This paper proposes an extension of the Multi-Index Stochastic Collocation (MISC) method for forward uncertainty quantification (UQ) problems in computational domains of shape other than a square or c...
CNR@People | DOI: 10.1016/j.cma.2019.03.042
2018, Journal article
A sparse-grid isogeometric solver
J. Beck, G. Sangalli, and L. Tamellini
Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can ben...
CNR@People | DOI: 10.1016/j.cma.2018.02.017
2018, Journal article
Convergence of sparse collocation for functions of countably many Gaussian random variables (with application to elliptic PDEs)
O.G. Ernst, B. Sprungk, and L. Tamellini
We give a convergence proof for the approximation by sparse collocation of Hilbert-space-valued functions depending on countably many Gaussian random variables. Such functions appear as solutions of e...
CNR@People | DOI: 10.1137/17M1123079
2018, Journal article
Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins
I. Colombo, F. Nobile, G. Porta, A. Scotti, and L. Tamellini
In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a coupled, time-dependent, n...
CNR@People | DOI: 10.1016/j.cma.2017.08.049
2017, Journal article
Optimal-order isogeometric collocation at Galerkin superconvergent points
M. Montardini, G. Sangalli, and L. Tamellini
In this paper we investigate numerically the order of convergence of an isogeometric collocation method that builds upon the least-squares collocation method presented in Anitescu et al. (2015) and th...
CNR@People | DOI: 10.1016/j.cma.2016.09.043
2016, Journal article
Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs
F. Nobile, L. Tamellini, and R. Tempone
In this work we provide a convergence analysis for the quasi-optimal version of the sparse-grids stochastic collocation method we presented in a previous work: "On the optimal polynomial approxim...
CNR@People | DOI: 10.1007/s00211-015-0773-y
2016, Journal article
Multi-index stochastic collocation convergence rates for random PDEs with parametric regularity
A.-L. Haji-Ali, F. Nobile, L. Tamellini, and R. Tempone
We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDE) with random data, where the random coefficient ...
CNR@People | DOI: 10.1007/s10208-016-9327-7
2016, Journal article
Multi-Index Stochastic Collocation for random PDEs
A.-L. Haji-Ali, F. Nobile, L. Tamellini, and R. Tempone
In this work we introduce the Multi-Index Stochastic Collocation method (MISC) for computing statistics of the solution of a PDE with random data. MISC is a combination technique based on mixed differ...
CNR@People | DOI: 10.1016/j.cma.2016.03.029
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
This work is a follow-up to our previous contribution ("Convergence of sparse collocation for functions of countably many Gaussian random variables (with application to elliptic PDEs)", SIAM...
CNR@People | DOI: 10.1007/978-3-030-81362-8_1
2016, Essay or book chapter
An adaptive sparse grid algorithm for elliptic PDEs with lognormal diffusion coefficient
F. Nobile, L. Tamellini, F. Tesei, and R. Tempone
In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature),obtaining an enhanced version capable of using non-nest...
CNR@People | DOI: 10.1007/978-3-319-28262-6_8
2015, Essay or book chapter
Comparison of Clenshaw-Curtis and Leja quasi-optimal sparse grids for the approximation of random PDEs
F. Nobile, L. Tamellini, and R. Tempone
In this work we compare different families of nested quadrature points, i.e. the classic Clenshaw-Curtis and various kinds of Leja points, in the context of the quasi-optimal sparse grid approximation...
CNR@People | DOI: 10.1007/978-3-319-19800-2_44
On expansions and nodes for sparse grid collocation of lognormal elliptic PDEs
O.G. Ernst, B. Sprungk, and L. Tamellini
This work is a follow-up to our previous contribution ("Convergence of sparse collocation for functions of countably many Gaussian random variables (with application to elliptic PDEs)", SIAM...
CNR@People | DOI: 10.1007/978-3-030-81362-8_1
2016, Essay or book chapter
An adaptive sparse grid algorithm for elliptic PDEs with lognormal diffusion coefficient
F. Nobile, L. Tamellini, F. Tesei, and R. Tempone
In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature),obtaining an enhanced version capable of using non-nest...
CNR@People | DOI: 10.1007/978-3-319-28262-6_8
2015, Essay or book chapter
Comparison of Clenshaw-Curtis and Leja quasi-optimal sparse grids for the approximation of random PDEs
F. Nobile, L. Tamellini, and R. Tempone
In this work we compare different families of nested quadrature points, i.e. the classic Clenshaw-Curtis and various kinds of Leja points, in the context of the quasi-optimal sparse grid approximation...
CNR@People | DOI: 10.1007/978-3-319-19800-2_44
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
This extended abstract is a summary of [4], where a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems is presented.Specifically, the performance ...
CNR@People | Link
2021, Conference proceedings
The Multi-index Stochastic Collocation method for uncertainty quantification of PDEs with random parameters
J. Beck, L. Tamellini, and R. Tempone
In many real-life applications, one needs to solvepartial differential equations (PDEs) to predict the behavior ofa system, most often by numerical methods. This goal is oftenhampered by the fact that...
CNR@People | Link
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
This paper presents a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems. Specifically, the performance of Multi-Index Stochastic Collocation (MIS...
CNR@People | DOI: 10.2514/6.2020-3160
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
This extended abstract is a summary of [4], where a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems is presented.Specifically, the performance ...
CNR@People | Link
2021, Conference proceedings
The Multi-index Stochastic Collocation method for uncertainty quantification of PDEs with random parameters
J. Beck, L. Tamellini, and R. Tempone
In many real-life applications, one needs to solvepartial differential equations (PDEs) to predict the behavior ofa system, most often by numerical methods. This goal is oftenhampered by the fact that...
CNR@People | Link
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
This paper presents a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems. Specifically, the performance of Multi-Index Stochastic Collocation (MIS...
CNR@People | DOI: 10.2514/6.2020-3160
