Nobile, F., Tamellini, L., Tesei, F., & Tempone, R. (2016). An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient. Sparse Grids and Applications - Stuttgart 2014, 191–220. https://doi.org/10.1007/978-3-319-28262-6_8
Matthies, H. G., Zander, E., Rosić, B. V., Litvinenko, A., & Pajonk, O. (2016). Inverse Problems in a Bayesian Setting. Computational Methods for Solids and Fluids, 245–286. https://doi.org/10.1007/978-3-319-27996-1_10


Nobile, F., Tamellini, L., & Tempone, R. (2015). Comparison of Clenshaw–Curtis and Leja Quasi-Optimal Sparse Grids for the Approximation of Random PDEs. Spectral and High Order Methods for Partial Differential Equations ICOSAHOM 2014, 475–482. https://doi.org/10.1007/978-3-319-19800-2_44
Motamed, M., Nobile, F., & Tempone, R. (2015). Analysis and Computation of Hyperbolic PDEs with Random Data. Encyclopedia of Applied and Computational Mathematics, 51–58. https://doi.org/10.1007/978-3-540-70529-1_527


Beck, J., Nobile, F., Tamellini, L., & Tempone, R. (2013). A Quasi-optimal Sparse Grids Procedure for Groundwater Flows. Spectral and High Order Methods for Partial Differential Equations - ICOSAHOM 2012, 1–16. https://doi.org/10.1007/978-3-319-01601-6_1
Litvinenko, A., & Matthies, H. G. (2013). Numerical Methods for Uncertainty Quantification and Bayesian Update in Aerodynamics. Management and Minimisation of Uncertainties and Errors in Numerical Aerodynamics, 265–282. https://doi.org/10.1007/978-3-642-36185-2_11
Zhu, Q., Tembine, H., & Başar, T. (2013). Hybrid Learning in Stochastic Games and Its Application in Network Security. Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, 303–329. https://doi.org/10.1002/9781118453988.ch14
Moon, K.-S., Szepessy, A., Tempone, R., & Zouraris, G. (2001). Hyperbolic Differential Equations and Adaptive Numerics. Theory and Numerics of Differential Equations, 231–280. https://doi.org/10.1007/978-3-662-04354-7_5


Tembine, H., Vilanova, P., & Debbah, M. (2012). Noisy Mean Field Game Model for Malware Propagation in Opportunistic Networks. Game Theory for Networks, 459–474. https://doi.org/10.1007/978-3-642-30373-9_32
Tembine, H. (2012). Distributed Strategic Learning for Wireless Engineers. https://doi.org/10.1201/b11896


Hoel, H., von Schwerin, E., Szepessy, A., & Tempone, R. (2011). Adaptive Multilevel Monte Carlo Simulation. Lecture Notes in Computational Science and Engineering, 217–234. https://doi.org/10.1007/978-3-642-21943-6_10