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
C. P., S. Safdar, A. Viguerie, and A. B. Gumel (2025). Analytical and numerical methods for spillover effects in prioritized PrEP for HIV prevention. 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