Y. Li, C. Zoccarato, C. P., L. Tamellini, G. Bru, C. Guardiola Albert, and P. Teatini (2025). Characterizing Aquifer Properties through a Sparse Grid-Based Bayesian Framework and InSAR Measurements: A Basin-scale application to Alto Guadalentín, Spain. Water Resources Research, 61, e2024WR038543. DOI
C. Kuehn, C. P., and E. Ullmann (2024). Uncertainty quantification analysis of bifurcations of the Allen--Cahn equation with random coefficients. Physica D: Nonlinear Phenomena 470, 134390. DOI
M. Chiappetta, C. P., L. Tamellini, A. Reali, F. Auricchio, and M. Carraturo (2024). Data-informed uncertainty quantification for laser-based powder bed fusion additive manufacturing. International Journal for Numerical Methods in Engineering 124, e7542. DOI arXiv
C. P. and L. Tamellini (2024). Algorithm 1040: The Sparse Grids Matlab kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification. ACM Transactions on Mathematical Software 50, 1-22. DOI
M. Chiappetta, C. P., M. Carraturo, L. Tamellini, A. Reali, and F. Auricchio (2023). Sparse-grids uncertainty quantification of part-scale additive manufacturing processes. International Journal of Mechanical Sciences 256, 108476. DOI arXiv
C. P., L. Tamellini, R. Pellegrini, R. Broglia, A. Serani, and M. Diez (2022).Comparing Multi-Index Stochastic Collocation and Multi-Fidelity Stochastic Radial Basis Functions for Forward Uncertainty Quantification of Ship Resistance. Engineering with Computers 39, 2209–2237. DOI arXiv
C. P., L. Tamellini, and R. Tempone (2021). A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology. Mathematical Biosciences 332, 108514. DOI arXiv
L. Einkemmer, A. Ostermann, and C. P. (2020). A low-rank projector-splitting integrator for the Vlasov–Maxwell equations with divergence correction. Journal of Computational Physics 403, 109063. DOI arXiv
A. Ostermann, C. P., and H. Walach (2019). Convergence of a low-rank Lie-Trotter splitting for stiff matrix differential equations. SIAM Journal on Numerical Analysis 57, 1947-1966. DOI arXiv
H. Mena, A. Ostermann, L.-M. Pfurtscheller, and C. P. (2018). Numerical low-rank approximation of matrix differential equations. Journal of Computational and Applied Mathematics 340, 602 - 614. DOI
M. Caliari, A. Ostermann, and C. P. (2017). A splitting approach for the magnetic Schrödinger equation. Journal of Computational and Applied Mathematics 316, 74-85. DOI arXiv
A. Viguerie, C. P., M.H. Islam, and E.U. Jacobson (2024). Input-output reduced order modeling for public health intervention evaluation. WCCM-PANACM 2024, 21--26/07/2024, Vancouver, Canada. DOI
C. P., L. Tamellini, R. Pellegrini, R. Broglia, A. Serani, and M. Diez (2020). Uncertainty Quantification of Ship Resistance via Multi-Index Stochastic Collocation and Radial Basis Function Surrogates: A Comparison. AIAA 2020-3160, Session: Multi-Fidelity Methods for Vehicle Applications I. DOI arXiv
L. Einkemmer, A. Ostermann, and C. P. (2019). A dynamical low-rank integrator for the Vlasov-Maxwell equations. Oberwolfach Reports 16, 379-381. DOI
L. Tamellini, Y. Li, C. P., C. Zoccarato (2025, November 11). Surrogate-model-based Bayesian estimation of Alto Guadalentı́n aquifer properties. SIAM News Blog. Link
C. P. and L. Tamellini (2023): The Sparse Grids Matlab Kit user manual – v.23-5 Robert. Available at https://sites.google.com/view/sparse-grids-kit
C. P. (2019): Dynamical low-rank approaches for differential equations. University of Innsbruck. Available online: Digital Library - University of Innsbruck
L. Tamellini, C. P., F. Nobile, B. Sprungk, G. Porta, D. Guignard, and F. Tesei (2009-today): Sparse Grids Matlab kit v.23-5 “Robert”. Available free of charge under BSD-2 Clause Licence here