81 citations · 231 across the 23 of their papers we have counts for
23 papers
Noise-robust multi-fidelity surrogate modelling for parametric partial differential equations
Benjamin M. Kent, Lorenzo Tamellini, Matteo Giacomini +1
We address the challenge of constructing noise-robust surrogate models for quantities of interest (QoIs) arising from parametric partial differential equations (PDEs), using multi-…
Democratizing Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard +22
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In thi…
Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing
Mihaela Chiappetta, Chiara Piazzola, Lorenzo Tamellini +3
We present an efficient approach to quantify the uncertainties associated with the numerical simulations of the laser-based powder bed fusion of metals processes. Our study focuses…
Lowering the Entry Bar to HPC-Scale Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Jean Benezech +3
Treating uncertainties in models is essential in many fields of science and engineering. Uncertainty quantification (UQ) on complex and computationally costly numerical models nece…
Uncertainty quantification in timber-like beams using sparse grids: theory and examples with off-the-shelf software utilization
Balduzzi Giuseppe, Bonizzoni Francesca, Tamellini Lorenzo
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, pr…
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
Emily A. Baker, Sauro Manenti, Alessandro Reali +3
Groundwater flow modeling is commonly used to calculate groundwater heads, estimate groundwater flow paths and travel times, and provide insights into solute transport processes wi…
Sparse-grids uncertainty quantification of part-scale additive manufacturing processes
Mihaela Chiappetta, Chiara Piazzola, Massimo Carraturo +3
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 m…
Combining the Morris Method and Multiple Error Metrics to Assess Aquifer Characteristics and Recharge in the Lower Ticino Basin, in Italy
Emily A. Baker, Alessandro Cappato, Sara Todeschini +4
Groundwater flow model accuracy is often limited by the uncertainty in model parameters that characterize aquifer properties and aquifer recharge. Aquifer properties such as hydrau…
The Sparse Grids Matlab kit -- a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
Chiara Piazzola, Lorenzo Tamellini
The Sparse Grids Matlab Kit provides a Matlab implementation of sparse grids, and can be used for approximating high-dimensional functions and, in particular, for surrogate-model-b…
Comparing Multi-Index Stochastic Collocation and Multi-Fidelity Stochastic Radial Basis Functions for Forward Uncertainty Quantification of Ship Resistance
Chiara Piazzola, Lorenzo Tamellini, Riccardo Pellegrini +3
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 q…
On the convergence of adaptive stochastic collocation for elliptic partial differential equations with affine diffusion
Martin Eigel, Oliver Ernst, Björn Sprungk +1
Convergence of an adaptive collocation method for the stationary parametric diffusion equation with finite-dimensional affine coefficient is shown. The adaptive algorithm relies on…
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
Chiara Piazzola, Lorenzo Tamellini, Raúl 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…
Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison
Chiara Piazzola, Lorenzo Tamellini, Riccardo Pellegrini +3
This paper presents a comparison of two methods for the forward uncertainty quantification (UQ) of complex industrial problems. Specifically, the performance of Multi-Index Stochas…
Compressive Isogeometric Analysis
Simone Brugiapaglia, Lorenzo Tamellini, Mattia 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 mo…
Parametric shape optimization for combined additive-subtractive manufacturing
Christian Altenhofen, Marco Attene, Oliver Barrowclough +6
In the industrial practice, additive manufacturing processes are often followed by post-processing operations such as subtractive machining, milling, etc. to achieve the desired su…
On Expansions and Nodes for Sparse Grid Collocation of Lognormal Elliptic PDEs
Oliver G. Ernst, Björn Sprungk, Lorenzo 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 PD…
IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains
Joakim Beck, Lorenzo Tamellini, Raúl 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 othe…
A sparse-grid isogeometric solver
Joakim Beck, Giancarlo Sangalli, Lorenzo 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…
Uncertainty Quantification of geochemical and mechanical compaction in layered sedimentary basins
Ivo Colombo, Fabio Nobile, Giovanni Porta +2
In this work we propose an Uncertainty Quantification methodology for sedimentary basins evolution under mechanical and geochemical compaction processes, which we model as a couple…
Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables (with Application to Elliptic PDEs)
Oliver G. Ernst, Björn Sprungk, Lorenzo 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 appe…
Optimal-order isogeometric collocation at Galerkin superconvergent points
Monica Montardini, Giancarlo Sangalli, Lorenzo 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 [1] and t…
Multi-index Stochastic Collocation convergence rates for random PDEs with parametric regularity
Abdul-Lateef Haji-Ali, Fabio Nobile, Lorenzo Tamellini +1
We analyze the recent Multi-index Stochastic Collocation (MISC) method for computing statistics of the solution of a partial differential equation (PDEs) with random data, where th…
Multi-Index Stochastic Collocation for random PDEs
Abdul-Lateef Haji-Ali, Fabio Nobile, Lorenzo Tamellini +1
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 b…