Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients. - Joakim Beck; Fabio Nobile; Lorenzo Tamellini; Raúl Tempone (Comput. Math. Appl., 2014)

Model Reduction Based on Proper Generalized Decomposition for the Stochastic Steady Incompressible Navier-Stokes Equations. - Lorenzo Tamellini; Olivier P. Le Maître; Anthony Nouy (SIAM J. Sci. Comput., 2014)

Multi-index Stochastic Collocation Convergence Rates for Random PDEs with Parametric Regularity. - Abdul-Lateef Haji-Ali; Fabio Nobile; Lorenzo Tamellini; Raúl Tempone (Found. Comput. Math., 2016)

Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs. - Fabio Nobile; Lorenzo Tamellini; Raúl Tempone (Numerische Mathematik, 2016)

Convergence of Sparse Collocation for Functions of Countably Many Gaussian Random Variables (with Application to Elliptic PDEs). - Oliver G. Ernst; Björn Sprungk; Lorenzo Tamellini (SIAM J. Numer. Anal., 2018)

On Expansions and Nodes for Sparse Grid Collocation of Lognormal Elliptic PDEs. - Oliver G. Ernst; Björn Sprungk; Lorenzo Tamellini (CoRR, 2019)

Parametric shape optimization for combined additive-subtractive manufacturing. - Christian Altenhofen; Marco Attene; Oliver J. D. Barrowclough; Michele Chiumenti; Marco Livesu; Federico Marini 003; Massimiliano Martinelli; Vibeke Skytt; Lorenzo Tamellini (CoRR, 2019)

Compressive isogeometric analysis. - Simone Brugiapaglia; Lorenzo Tamellini; Mattia Tani (Comput. Math. Appl., 2020)

Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison. - Chiara Piazzola; Lorenzo Tamellini; Riccardo Pellegrini; Riccardo Broglia; Andrea Serani; Matteo Diez (CoRR, 2020)

Computational Approaches for Uncertainty Quantification of Naval Engineering Problems. - Riccardo Broglia; Matteo Diez; Lorenzo Tamellini (ERCIM News, 2020)

The Sparse Grids Matlab kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification. - Chiara Piazzola; Lorenzo Tamellini (CoRR, 2022)

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; Lorenzo Tamellini; Giancarlo Sangalli; Alessandro Reali; Sauro Manenti (CoRR, 2022)

Inverse and forward sparse-grids-based uncertainty quantification analysis of laser-based powder bed fusion of metals. - Mihaela Chiappetta; Chiara Piazzola; Massimo Carraturo; Lorenzo Tamellini; Alessandro Reali; Ferdinando Auricchio (CoRR, 2022)

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; Giancarlo Sangalli; Lorenzo Tamellini; Sara Todeschini (CoRR, 2022)

Uncertainty quantification in timber-like beams using sparse grids: theory and examples with off-the-shelf software utilization. - Giuseppe Balduzzi; Francesca Bonizzoni; Lorenzo Tamellini (CoRR, 2022)

On the Convergence of Adaptive Stochastic Collocation for Elliptic Partial Differential Equations with Affine Diffusion. - Martin Eigel; Oliver G. Ernst; Björn Sprungk; Lorenzo Tamellini (SIAM J. Numer. Anal., 2022)

Lowering the Entry Bar to HPC-Scale Uncertainty Quantification. - Linus Seelinger; Anne Reinarz; Jean Bénézech; Mikkel Bue Lykkegaard; Lorenzo Tamellini; Robert Scheichl (CoRR, 2023)

Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing. - Mihaela Chiappetta; Chiara Piazzola; Lorenzo Tamellini; Alessandro Reali; Ferdinando Auricchio; Massimo Carraturo (CoRR, 2023)

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; Riccardo Broglia; Andrea Serani; Matteo Diez (Eng. Comput., 2023)

Democratizing Uncertainty Quantification. - Linus Seelinger; Anne Reinarz; Mikkel Bue Lykkegaard; Amal Mohammed A. Alghamdi; David Aristoff; Wolfgang Bangerth; Jean Bénézech; Matteo Diez; Kurt Frey; John D. Jakeman; Jakob Sauer Jørgensen; Ki-Tae Kim; et al. (CoRR, 2024)

Two Nitsche-based mixed finite element discretizations for the seepage problem in Richards' equation. - Federico Gatti; Andrea Bressan; Alessio Fumagalli; Domenico Gallipoli; Leonardo Maria Lalicata; Simone Pittaluga; Lorenzo Tamellini (CoRR, 2024)

An efficient slope stability algorithm with physically consistent parametrisation of slip surfaces. - Leonardo Maria Lalicata; Andrea Bressan; Simone Pittaluga; Lorenzo Tamellini; Domenico Gallipoli (CoRR, 2024)

Algorithm 1040: The Sparse Grids Matlab Kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification. - Chiara Piazzola; Lorenzo Tamellini (ACM Trans. Math. Softw., 2024)

Democratizing uncertainty quantification. - Linus Seelinger; Anne Reinarz; Mikkel Bue Lykkegaard; Robert Akers; Amal Mohammed A. Alghamdi; David Aristoff; Wolfgang Bangerth; Jean Bénézech; Matteo Diez; Kurt Frey; John D. Jakeman; Jakob Sauer Jørgensen; et al. (J. Comput. Phys., 2025)

Noise-robust multi-fidelity surrogate modelling for parametric partial differential equations. - Benjamin M. Kent; Lorenzo Tamellini; Matteo Giacomini; Antonio Huerta (CoRR, 2025)