2024

Bayer, C., Ben Hammouda, C., & Tempone, R. (2024). Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities. SIAM Journal on Scientific Computing, 46(3), A1514–A1548. https://doi.org/10.1137/22m1495718
Carlon, A. G., Espath, L., & Tempone, R. (2024). Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization. Optimization Methods and Software, 1–31. https://doi.org/10.1080/10556788.2024.2339226
Logashenko, D., Litvinenko, A., Tempone, R., Vasilyeva, E., & Wittum, G. (2024). Uncertainty quantification in the Henry problem using the multilevel Monte Carlo method. Journal of Computational Physics, 503, 112854. https://doi.org/10.1016/j.jcp.2024.112854

2023

Madrigal-Cianci, J. P., Nobile, F., & Tempone, R. (2023). Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings. SIAM/ASA Journal on Uncertainty Quantification, 11(1), 91–138. https://doi.org/10.1137/21m1420927
Hoang, T.-V., Krumscheid, S., Matthies, H. G., & Tempone, R. (2023). Machine learning-based conditional mean filter: A generalization of the ensemble Kalman filter for nonlinear data assimilation. Foundations of Data Science, 5(1), 56–80. https://doi.org/10.3934/fods.2022016
Jasra, A., Kamatani, K., & Maama, M. (2023). Multilevel Particle Filters for a Class of Partially Observed Piecewise Deterministic Markov Processes (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2309.02998
Bayer, C., Ben Hammouda, C., Papapantoleon, A., Samet, M., & Tempone, R. (2023). Optimal damping with a hierarchical adaptive quadrature for efficient Fourier pricing of multi-asset options in Lévy models. Journal of Computational Finance. https://doi.org/10.21314/jcf.2023.012
Liu, Y., & Tempone, R. (2023). Nonasymptotic Convergence Rate of Quasi-Monte Carlo: Applications to Linear Elliptic PDEs with Lognormal Coefficients and Importance Samplings (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2310.14351
Bartuska, A., Carlon, A. G., Espath, L., Krumscheid, S., & Tempone, R. (2023). Double-loop quasi-Monte Carlo estimator for nested integration (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2302.14119

2022

Bayer, C., Ben Hammouda, C., & Tempone, R. (2022). Numerical smoothing with hierarchical adaptive sparse grids and quasi-Monte Carlo methods for efficient option pricing. Quantitative Finance, 1–19. https://doi.org/10.1080/14697688.2022.2135455
Kammonen, A., Kiessling, J., Plecháč, P., Sandberg, M., Szepessy, A., & Tempone, R. (2022). Smaller generalization error derived for a deep residual neural network compared with shallow networks. IMA Journal of Numerical Analysis. https://doi.org/10.1093/imanum/drac049
Beck, J., Liu, Y., von Schwerin, E., & Tempone, R. (2022). Goal-oriented adaptive finite element multilevel Monte Carlo with convergence rates. Computer Methods in Applied Mechanics and Engineering, 115582. https://doi.org/10.1016/j.cma.2022.115582
Hoang, T.-V., Krumscheid, S., Matthies, H. G., & Tempone, R. (2022). Machine learning-based conditional mean filter: A generalization of the ensemble Kalman filter for nonlinear data assimilation. Foundations of Data Science, 0(0), 0. https://doi.org/10.3934/fods.2022016
Hoel, H., Shaimerdenova, G., & Tempone, R. (2022). Multi-index ensemble Kalman filtering. Journal of Computational Physics, 470, 111561. https://doi.org/10.1016/j.jcp.2022.111561
Bartuska, A., Espath, L., & Tempone, R. (2022). Small-noise approximation for Bayesian optimal experimental design with nuisance uncertainty. Computer Methods in Applied Mechanics and Engineering, 399, 115320. https://doi.org/10.1016/j.cma.2022.115320
Ballesio, M., Jasra, A., von Schwerin, E., & Tempone, R. (2022). A Wasserstein coupled particle filter for multilevel estimation. Stochastic Analysis and Applications, 1–40. https://doi.org/10.1080/07362994.2022.2081181
Carlon, A. G., Kroetz, H. M., Torii, A. J., Lopez, R. H., & Miguel, L. F. F. (2022). Risk optimization using the Chernoff bound and stochastic gradient descent. Reliability Engineering & System Safety, 223, 108512. https://doi.org/10.1016/j.ress.2022.108512
Cramer, E., Mitsos, A., Tempone, R., & Dahmen, M. (2022). Principal component density estimation for scenario generation using normalizing flows. Data-Centric Engineering, 3. https://doi.org/10.1017/dce.2022.7

2021

Kabanov, D. I., Espath, L., Kiessling, J., & Tempone, R. F. (2021). Estimating divergence‐free flows via neural networks. PAMM, 21(1). Portico. https://doi.org/10.1002/pamm.202100173
Kiessling, J., Ström, E., & Tempone, R. (2021). Wind field reconstruction with adaptive random Fourier features. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 477(2255). https://doi.org/10.1098/rspa.2021.0236
Ben Rached, N., Haji-Ali, A.-L., Rubino, G., & Tempone, R. (2021). Efficient importance sampling for large sums of independent and identically distributed random variables. Statistics and Computing, 31(6). https://doi.org/10.1007/s11222-021-10055-1
Espath, L., Kabanov, D., Kiessling, J., & Tempone, R. (2021). Statistical learning for fluid flows: Sparse Fourier divergence-free approximations. Physics of Fluids, 33(9), 097108. https://doi.org/10.1063/5.0064862
Latz, J., Madrigal-Cianci, J. P., Nobile, F., & Tempone, R. (2021). Generalized parallel tempering on Bayesian inverse problems. Statistics and Computing, 31(5). https://doi.org/10.1007/s11222-021-10042-6
Caballero, R., Kebaier, A., Scavino, M., & Tempone, R. (2021). Quantifying uncertainty with a derivative tracking SDE model and application to wind power forecast data. Statistics and Computing, 31(5). https://doi.org/10.1007/s11222-021-10040-8
Castrillón-Candás, J. E., Nobile, F., & Tempone, R. F. (2021). A hybrid collocation-perturbation approach for PDEs with random domains. Advances in Computational Mathematics, 47(3). https://doi.org/10.1007/s10444-021-09859-6
Litvinenko, A., Yucel, A., Bagci, H., Oppelstrup, J., Michielssen, E., & Tempone, R. (2021). MLMC method to estimate propagation of uncertainties in electromagnetic fields scattered from objects of uncertain shapes. PAMM, 20(1). Portico. https://doi.org/10.1002/pamm.202000064

2020

Hoel, H., Shaimerdenova, G., & Tempone, R. (2020). Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators. Foundations of Data Science, 2(4), 351–390. https://doi.org/10.3934/fods.2020017
Chernov, A., Hoel, H., Law, K. J. H., Nobile, F., & Tempone, R. (2020). Multilevel ensemble Kalman filtering for spatio-temporal processes. Numerische Mathematik, 147(1), 71–125. https://doi.org/10.1007/s00211-020-01159-3
Piazzola, C., Tamellini, L., & Tempone, R. (2021). A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology. Mathematical Biosciences, 332, 108514. https://doi.org/10.1016/j.mbs.2020.108514
Issaid, C. B., Alouini, M.-S., & Tempone, R. (2021). Efficient Importance Sampling for the Left Tail of Positive Gaussian Quadratic Forms. IEEE Wireless Communications Letters, 10(3), 527–531. https://doi.org/10.1109/lwc.2020.3036588
Ben Hammouda, C., Ben Rached, N., & Tempone, R. (2020). Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks. Statistics and Computing, 30(6), 1665–1689. https://doi.org/10.1007/s11222-020-09965-3
Carlon, A. G., Torii, A. J., Lopez, R. H., & de Cursi, J. E. S. (2020). Stochastic Gradient Descent for Risk Optimization. Proceedings of the 5th International Symposium on Uncertainty Quantification and Stochastic Modelling, 424–435. https://doi.org/10.1007/978-3-030-53669-5_31
Ben Rached, N., Kammoun, A., Alouini, M.-S., & Tempone, R. (2020). An Accurate Sample Rejection Estimator of the Outage Probability With Equal Gain Combining. IEEE Open Journal of the Communications Society, 1, 1022–1034. https://doi.org/10.1109/ojcoms.2020.3010649
Bayer, C., Tempone, R., & Wolfers, S. (2020). Pricing American options by exercise rate optimization. Quantitative Finance, 20(11), 1749–1760. https://doi.org/10.1080/14697688.2020.1750678
Bayer, C., Ben Hammouda, C., & Tempone, R. (2020). Hierarchical adaptive sparse grids and quasi-Monte Carlo for option pricing under the rough Bergomi model. Quantitative Finance, 20(9), 1457–1473. https://doi.org/10.1080/14697688.2020.1744700
Beck, J., Mansour Dia, B., Espath, L., & Tempone, R. (2020). Multilevel double loop Monte Carlo and stochastic collocation methods with importance sampling for Bayesian optimal experimental design. International Journal for Numerical Methods in Engineering, 121(15), 3482–3503. Portico. https://doi.org/10.1002/nme.6367
Rached, N. B., Mackinlay, D., Botev, Z., Tempone, R., & Alouini, M.-S. (2020). A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems. IEEE Transactions on Wireless Communications, 19(7), 4353–4362. https://doi.org/10.1109/twc.2020.2982649
Haji-Ali, A.-L., Nobile, F., Tempone, R., & Wolfers, S. (2020). Multilevel weighted least squares polynomial approximation. ESAIM: Mathematical Modelling and Numerical Analysis, 54(2), 649–677. https://doi.org/10.1051/m2an/2019045
Litvinenko, A., Logashenko, D., Tempone, R., Wittum, G., & Keyes, D. (2020). Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability. GEM - International Journal on Geomathematics, 11(1). https://doi.org/10.1007/s13137-020-0147-1
Carlon, A. G., Dia, B. M., Espath, L., Lopez, R. H., & Tempone, R. (2020). Nesterov-aided stochastic gradient methods using Laplace approximation for Bayesian design optimization. Computer Methods in Applied Mechanics and Engineering, 363, 112909. https://doi.org/10.1016/j.cma.2020.112909

2019

Litvinenko, A., Logashenko, D., Tempone, R., Wittum, G., & Keyes, D. (2019). Efficient Simulations for Contamination of Groundwater Aquifers under Uncertainties. PAMM, 19(1). Portico. https://doi.org/10.1002/pamm.201900023
Ballesio, M., Beck, J., Pandey, A., Parisi, L., von Schwerin, E., & Tempone, R. (2019). Multilevel Monte Carlo acceleration of seismic wave propagation under uncertainty. GEM - International Journal on Geomathematics, 10(1). https://doi.org/10.1007/s13137-019-0135-5
Litvinenko, A., Kriemann, R., Genton, M. G., Sun, Y., & Keyes, D. E. (2020). HLIBCov: Parallel hierarchical matrix approximation of large covariance matrices and likelihoods with applications in parameter identification. MethodsX, 7, 100600. https://doi.org/10.1016/j.mex.2019.07.001
Beck, J., Tamellini, L., & Tempone, R. (2019). IGA-based multi-index stochastic collocation for random PDEs on arbitrary domains. Computer Methods in Applied Mechanics and Engineering, 351, 330–350. https://doi.org/10.1016/j.cma.2019.03.042
Capriotti, L., Jiang, Y., & Shaimerdenova, G. (2019) Approximation methods for inhomogeneous geometric Brownian motion. International Journal of Theoretical and Applied Finance, 22(02), 1850055. https://doi.org/10.1142/s0219024918500553
Carlon, A. G., Lopez, R. H., Espath, L. F. R., Miguel, L. F. F., & Beck, A. T. (2019). A stochastic gradient approach for the reliability maximization of passively controlled structures. Engineering Structures, 186, 1–12. https://doi.org/10.1016/j.engstruct.2019.01.121
Litvinenko, A., Yucel, A. C., Bagci, H., Oppelstrup, J., Michielssen, E., & Tempone, R. (2019). Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method. IEEE Journal on Multiscale and Multiphysics Computational Techniques, 4, 37–50. https://doi.org/10.1109/jmmct.2019.2897490
Jasra, A., Jo, S., Nott, D., Shoemaker, C., & Tempone, R. (2019). Multilevel Monte Carlo in approximate Bayesian computation. Stochastic Analysis and Applications, 37(3), 346–360. https://doi.org/10.1080/07362994.2019.1566006

2018

Babuška, I., Sawlan, Z., Scavino, M., Szabó, B., & Tempone, R. (2019). Spatial Poisson processes for fatigue crack initiation. Computer Methods in Applied Mechanics and Engineering, 345, 454–475. https://doi.org/10.1016/j.cma.2018.11.007
Clavijo, S. P., Sarmiento, A. F., Espath, L. F. R., Dalcin, L., Cortes, A. M. A., & Calo, V. M. (2019). Reactive n-species Cahn–Hilliard system: A thermodynamically-consistent model for reversible chemical reactions. Journal of Computational and Applied Mathematics, 350, 143–154. https://doi.org/10.1016/j.cam.2018.10.007
Ben Rached, N., Botev, Z., Kammoun, A., Alouini, M.-S., & Tempone, R. (2018). On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances. IEEE Transactions on Wireless Communications, 17(11), 7801–7813. https://doi.org/10.1109/twc.2018.2871201
Rached, N. B., Botev, Z., Kammoun, A., Alouini, M.-S., & Tempone, R. (2018). Importance Sampling Estimator of Outage Probability under Generalized Selection Combining Model. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2018.8462177
Bayer, C., Häppölä, J., & Tempone, R. (2018). Implied stopping rules for American basket options from Markovian projection. Quantitative Finance, 19(3), 371–390. https://doi.org/10.1080/14697688.2018.1481290
Iglesias, M., Sawlan, Z., Scavino, M., Tempone, R., & Wood, C. (2018). Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: application to heat transfer in building walls. Inverse Problems, 34(7), 075008. https://doi.org/10.1088/1361-6420/aac224
Alouini, M.-S., Ben Rached, N., Kammoun, A., & Tempone, R. (2018). On the efficient simulation of the left-tail of the sum of correlated log-normal variates. Monte Carlo Methods and Applications, 24(2), 101–115. https://doi.org/10.1515/mcma-2018-0009
Beck, J., Sangalli, G., & Tamellini, L. (2018). A sparse-grid isogeometric solver. Computer Methods in Applied Mechanics and Engineering, 335, 128–151. https://doi.org/10.1016/j.cma.2018.02.017
Beck, J., Dia, B. M., Espath, L. F. R., Long, Q., & Tempone, R. (2018). Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain. Computer Methods in Applied Mechanics and Engineering, 334, 523–553. https://doi.org/10.1016/j.cma.2018.01.053

2017

Ben Issaid, C., Alouini, M.-S., & Tempone, R. (2018). On the Fast and Precise Evaluation of the Outage Probability of Diversity Receivers Over $\alpha -\mu $ , $\kappa -\mu $ , and $\eta -\mu $ Fading Channels. IEEE Transactions on Wireless Communications, 17(2), 1255–1268. https://doi.org/10.1109/twc.2017.2777465
Nobile, F., Tempone, R., & Wolfers, S. (2017). Sparse approximation of multilinear problems with applications to kernel-based methods in UQ. Numerische Mathematik, 139(1), 247–280. https://doi.org/10.1007/s00211-017-0932-4
Francisco, E. P., Espath, L. F. R., Laizet, S., & Silvestrini, J. H. (2017). Reynolds number and settling velocity influence for finite-release particle-laden gravity currents in a basin. Computers & Geosciences, 110, 1–9. https://doi.org/10.1016/j.cageo.2017.09.010
Iglesias, M., Sawlan, Z., Scavino, M., Tempone, R., & Wood, C. (2018). Bayesian inferences of the thermal properties of a wall using temperature and heat flux measurements. International Journal of Heat and Mass Transfer, 116, 417–431. https://doi.org/10.1016/j.ijheatmasstransfer.2017.09.022
Haji-Ali, A.-L., & Tempone, R. (2017). Multilevel and Multi-index Monte Carlo methods for the McKean–Vlasov equation. Statistics and Computing, 28(4), 923–935. https://doi.org/10.1007/s11222-017-9771-5
Espath, L. F. R., Sarmiento, A. F., Dalcin, L., & Calo, V. M. (2017). On the thermodynamics of the Swift–Hohenberg theory. Continuum Mechanics and Thermodynamics, 29(6), 1335–1345. https://doi.org/10.1007/s00161-017-0581-y
Liu, D., Litvinenko, A., Schillings, C., & Schulz, V. (2017). Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties---Comparison of Approaches. SIAM/ASA Journal on Uncertainty Quantification, 5(1), 334–352. https://doi.org/10.1137/15m1050239
Francisco, E. P., Espath, L. F. R., & Silvestrini, J. H. (2017). Direct numerical simulation of bi-disperse particle-laden gravity currents in the channel configuration. Applied Mathematical Modelling, 49, 739–752. https://doi.org/10.1016/j.apm.2017.02.051
Ben Rached, N., Kammoun, A., Alouini, M.-S., & Tempone, R. (2017). On the Efficient Simulation of Outage Probability in a Log-Normal Fading Environment. IEEE Transactions on Communications, 65(6), 2583–2593. https://doi.org/10.1109/tcomm.2017.2669979

2016

Hall, E. J., Hoel, H., Sandberg, M., Szepessy, A., & Tempone, R. (2016). Computable Error Estimates for Finite Element Approximations of Elliptic Partial Differential Equations with Rough Stochastic Data. SIAM Journal on Scientific Computing, 38(6), A3773–A3807. https://doi.org/10.1137/15m1044266
Crocce, F., Häppölä, J., Kiessling, J., & Tempone, R. (2016). Error analysis in Fourier methods for option pricing. The Journal of Computational Finance, 21(1). https://doi.org/10.21314/jcf.2016.327
Ben Rached, N., Benkhelifa, F., Kammoun, A., Alouini, M.-S., & Tempone, R. (2016). On the generalization of the hazard rate twisting-based simulation approach. Statistics and Computing, 28(1), 61–75. https://doi.org/10.1007/s11222-016-9716-4
Chen, Y., Keyes, D., Law, K. J. H., & Ltaief, H. (2016). Accelerated Dimension-Independent Adaptive Metropolis. SIAM Journal on Scientific Computing, 38(5), S539–S565. https://doi.org/10.1137/15m1026432
Kalligiannaki, E., Chazirakis, A., Tsourtis, A., Katsoulakis, M. A., Plecháč, P., & Harmandaris, V. (2016). Parametrizing coarse grained models for molecular systems at equilibrium. The European Physical Journal Special Topics, 225(8–9), 1347–1372. https://doi.org/10.1140/epjst/e2016-60145-x
Malenova, G., Motamed, M., Runborg, O., & Tempone, R. (2016). A Sparse Stochastic Collocation Technique for High-Frequency Wave Propagation with Uncertainty. SIAM/ASA Journal on Uncertainty Quantification, 4(1), 1084–1110. https://doi.org/10.1137/15m1029230
Beskos, A., Jasra, A., Law, K., Tempone, R., & Zhou, Y. (2017). Multilevel sequential Monte Carlo samplers. Stochastic Processes and Their Applications, 127(5), 1417–1440. https://doi.org/10.1016/j.spa.2016.08.004
Haji-Ali, A.-L., Nobile, F., Tamellini, L., & Tempone, R. (2016). Multi-index Stochastic Collocation Convergence Rates for Random PDEs with Parametric Regularity. Foundations of Computational Mathematics, 16(6), 1555–1605. https://doi.org/10.1007/s10208-016-9327-7
Moraes, A., Tempone, R., & Vilanova, P. (2016). A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks. SIAM Journal on Scientific Computing, 38(4), A2091–A2117. https://doi.org/10.1137/140972081
Ben Hammouda, C., Moraes, A., & Tempone, R. (2016). Multilevel hybrid split-step implicit tau-leap. Numerical Algorithms, 74(2), 527–560. https://doi.org/10.1007/s11075-016-0158-z
Hoel, H., Law, K. J. H., & Tempone, R. (2016). Multilevel ensemble Kalman filtering. SIAM Journal on Numerical Analysis, 54(3), 1813–1839. https://doi.org/10.1137/15m100955x
Espath, L. F. R., Sarmiento, A. F., Vignal, P., Varga, B. O. N., Cortes, A. M. A., Dalcin, L., & Calo, V. M. (2016). Energy exchange analysis in droplet dynamics via the Navier–Stokes–Cahn–Hilliard model. Journal of Fluid Mechanics, 797, 389–430. https://doi.org/10.1017/jfm.2016.277
Ruggeri, F., Sawlan, Z., Scavino, M., & Tempone, R. (2017). A Hierarchical Bayesian Setting for an Inverse Problem in Linear Parabolic PDEs with Noisy Boundary Conditions. Bayesian Analysis, 12(2). https://doi.org/10.1214/16-ba1007
Ruggeri, F., Sawlan, Z., Scavino, M., & Tempone, R. (2017). A Hierarchical Bayesian Setting for an Inverse Problem in Linear Parabolic PDEs with Noisy Boundary Conditions. Bayesian Analysis, 12(2). https://doi.org/10.1214/16-ba1007
Ben Rached, N., Kammoun, A., Alouini, M.-S., & Tempone, R. (2015). An Efficient Simulation Scheme of the Outage Probability with Co-Channel Interference. 2015 IEEE Global Communications Conference (GLOBECOM). https://doi.org/10.1109/glocom.2015.7417652
Haji-Ali, A.-L., Nobile, F., Tamellini, L., & Tempone, R. (2016). Multi-Index Stochastic Collocation for random PDEs. Computer Methods in Applied Mechanics and Engineering, 306, 95–122. https://doi.org/10.1016/j.cma.2016.03.029
Castrillón-Candás, J. E., Nobile, F., & Tempone, R. F. (2016). Analytic regularity and collocation approximation for elliptic PDEs with random domain deformations. Computers & Mathematics with Applications, 71(6), 1173–1197. https://doi.org/10.1016/j.camwa.2016.01.005
Law, K. J. H., Sanz-Alonso, D., Shukla, A., & Stuart, A. M. (2016). Filter accuracy for the Lorenz 96 model: Fixed versus adaptive observation operators. Physica D: Nonlinear Phenomena, 325, 1–13. https://doi.org/10.1016/j.physd.2015.12.008
Babuška, I., Sawlan, Z., Scavino, M., Szabó, B., & Tempone, R. (2016). Bayesian inference and model comparison for metallic fatigue data. Computer Methods in Applied Mechanics and Engineering, 304, 171–196. https://doi.org/10.1016/j.cma.2016.02.013
Bisetti, F., Kim, D., Knio, O., Long, Q., & Tempone, R. (2016). Optimal Bayesian Experimental Design for Priors of Compact Support with Application to Shock-Tube Experiments for Combustion Kinetics. International Journal for Numerical Methods in Engineering, 108(2), 136–155. Portico. https://doi.org/10.1002/nme.5211
Icardi, M., Boccardo, G., & Tempone, R. (2016). On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo. Advances in Water Resources, 95, 46–60. https://doi.org/10.1016/j.advwatres.2016.01.004

2015

Ben Rached, N., Kammoun, A., Alouini, M.-S., & Tempone, R. (2016). Unified Importance Sampling Schemes for Efficient Simulation of Outage Capacity Over Generalized Fading Channels. IEEE Journal of Selected Topics in Signal Processing, 10(2), 376–388. https://doi.org/10.1109/jstsp.2015.2500201
Dolgov, S., Khoromskij, B. N., Litvinenko, A., & Matthies, H. G. (2015). Polynomial Chaos Expansion of Random Coefficients and the Solution of Stochastic Partial Differential Equations in the Tensor Train Format. SIAM/ASA Journal on Uncertainty Quantification, 3(1), 1109–1135. https://doi.org/10.1137/140972536
Nobile, F., Tamellini, L., & Tempone, R. (2015). Convergence of quasi-optimal sparse-grid approximation of Hilbert-space-valued functions: application to random elliptic PDEs. Numerische Mathematik, 134(2), 343–388. https://doi.org/10.1007/s00211-015-0773-y
Motamed, M., Nobile, F., & Tempone, R. (2015). Analysis and computation of the elastic wave equation with random coefficients. Computers & Mathematics with Applications, 70(10), 2454–2473. https://doi.org/10.1016/j.camwa.2015.09.013
Migliorati, G., Nobile, F., & Tempone, R. (2015). Convergence estimates in probability and in expectation for discrete least squares with noisy evaluations at random points. Journal of Multivariate Analysis, 142, 167–182. https://doi.org/10.1016/j.jmva.2015.08.009
Le Maître, O. P., Knio, O. M., & Moraes, A. (2015). Variance decomposition in stochastic simulators. The Journal of Chemical Physics, 142(24), 244115. Portico. https://doi.org/10.1063/1.4922922
Haji-Ali, A.-L., Nobile, F., & Tempone, R. (2015). Multi-index Monte Carlo: when sparsity meets sampling. Numerische Mathematik, 132(4), 767–806. https://doi.org/10.1007/s00211-015-0734-5
Haji-Ali, A.-L., Nobile, F., von Schwerin, E., & Tempone, R. (2015). Optimization of mesh hierarchies in multilevel Monte Carlo samplers. Stochastics and Partial Differential Equations Analysis and Computations, 4(1), 76–112. https://doi.org/10.1007/s40072-015-0049-7
Karlsson, J., Larsson, S., Sandberg, M., Szepessy, A., & Tempone, R. (2015). An Error Estimate for Symplectic Euler Approximation of Optimal Control Problems. SIAM Journal on Scientific Computing, 37(2), A946–A969. https://doi.org/10.1137/140959481
Moraes, A., Tempone, R., & Vilanova, P. (2015). Multilevel hybrid Chernoff tau-leap. BIT Numerical Mathematics, 56(1), 189–239. https://doi.org/10.1007/s10543-015-0556-y
Chkifa, A., Cohen, A., Migliorati, G., Nobile, F., & Tempone, R. (2015). Discrete least squares polynomial approximation with random evaluations − application to parametric and stochastic elliptic PDEs. ESAIM: Mathematical Modelling and Numerical Analysis, 49(3), 815–837. https://doi.org/10.1051/m2an/2014050
Long, Q., Motamed, M., & Tempone, R. (2015). Fast Bayesian optimal experimental design for seismic source inversion. Computer Methods in Applied Mechanics and Engineering, 291, 123–145. https://doi.org/10.1016/j.cma.2015.03.021
Djehiche, B., Tembine, H., & Tempone, R. (2014). A stochastic maximum principle for risk-sensitive mean-field-type control. 53rd IEEE Conference on Decision and Control. https://doi.org/10.1109/cdc.2014.7039929

2014

Long, Q., Scavino, M., Tempone, R., & Wang, S. (2015). A Laplace method for under-determined Bayesian optimal experimental designs. Computer Methods in Applied Mechanics and Engineering, 285, 849–876. https://doi.org/10.1016/j.cma.2014.12.008
Giraldi, L., Litvinenko, A., Liu, D., Matthies, H. G., & Nouy, A. (2014). To Be or Not to Be Intrusive? The Solution of Parametric and Stochastic Equations---the “Plain Vanilla” Galerkin Case. SIAM Journal on Scientific Computing, 36(6), A2720–A2744. https://doi.org/10.1137/130942802
Giraldi, L., Litvinenko, A., Liu, D., Matthies, H. G., & Nouy, A. (2014). To Be or Not to Be Intrusive? The Solution of Parametric and Stochastic Equations---the “Plain Vanilla” Galerkin Case. SIAM Journal on Scientific Computing, 36(6), A2720–A2744. https://doi.org/10.1137/130942802
Selvakumaran, L., Long, Q., Prudhomme, S., & Lubineau, G. (2015). On the detectability of transverse cracks in laminated composites using electrical potential change measurements. Composite Structures, 121, 237–246. https://doi.org/10.1016/j.compstruct.2014.11.008
Selvakumaran, L., Long, Q., Prudhomme, S., & Lubineau, G. (2015). On the detectability of transverse cracks in laminated composites using electrical potential change measurements. Composite Structures, 121, 237–246. https://doi.org/10.1016/j.compstruct.2014.11.008
Rached, N. B., Kammoun, A., Alouini, M.-S., & Tempone, R. (2015). An Improved Hazard Rate Twisting Approach for the Statistic of the Sum of Subexponential Variates. IEEE Communications Letters, 19(1), 14–17. https://doi.org/10.1109/lcomm.2014.2368562
Tembine, H. (2014). Nonasymptotic Mean-Field Games. IFAC Proceedings Volumes, 47(3), 8989–8994. https://doi.org/10.3182/20140824-6-za-1003.01869
Tembine, H. (2014). Nonasymptotic Mean-Field Games. IFAC Proceedings Volumes, 47(3), 8989–8994. https://doi.org/10.3182/20140824-6-za-1003.01869
Kelly, D. T. B., Law, K. J. H., & Stuart, A. M. (2014). Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time. Nonlinearity, 27(10), 2579–2603. https://doi.org/10.1088/0951-7715/27/10/2579
Kelly, D. T. B., Law, K. J. H., & Stuart, A. M. (2014). Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time. Nonlinearity, 27(10), 2579–2603. https://doi.org/10.1088/0951-7715/27/10/2579
Collier, N., Haji-Ali, A.-L., Nobile, F., von Schwerin, E., & Tempone, R. (2014). A continuation multilevel Monte Carlo algorithm. BIT Numerical Mathematics, 55(2), 399–432. https://doi.org/10.1007/s10543-014-0511-3
Annunziato, M., Borzì, A., Nobile, F., & Tempone, R. (2014). On the Connection between the Hamilton-Jacobi-Bellman and the Fokker-Planck Control Frameworks. Applied Mathematics, 05(16), 2476–2484. https://doi.org/10.4236/am.2014.516239
Annunziato, M., Borzì, A., Nobile, F., & Tempone, R. (2014). On the Connection between the Hamilton-Jacobi-Bellman and the Fokker-Planck Control Frameworks. Applied Mathematics, 05(16), 2476–2484. https://doi.org/10.4236/am.2014.516239
Icardi, M., Boccardo, G., Marchisio, D. L., Tosco, T., & Sethi, R. (2014). Pore-scale simulation of fluid flow and solute dispersion in three-dimensional porous media. Physical Review E, 90(1). https://doi.org/10.1103/physreve.90.013032
Icardi, M., Boccardo, G., Marchisio, D. L., Tosco, T., & Sethi, R. (2014). Pore-scale simulation of fluid flow and solute dispersion in three-dimensional porous media. Physical Review E, 90(1). https://doi.org/10.1103/physreve.90.013032
Tembine, H. (2014). Energy-constrained Mean Field Games in Wireless Networks. Strategic Behavior and the Environment, 4(2), 187–211. https://doi.org/10.1561/102.00000040
Tembine, H. (2014). Energy-constrained Mean Field Games in Wireless Networks. Strategic Behavior and the Environment, 4(2), 187–211. https://doi.org/10.1561/102.00000040
Saad, B., & Saad, M. (2014). A combined finite volume–nonconforming finite element scheme for compressible two phase flow in porous media. Numerische Mathematik, 129(4), 691–722. https://doi.org/10.1007/s00211-014-0651-z
Saad, B., & Saad, M. (2014). A combined finite volume–nonconforming finite element scheme for compressible two phase flow in porous media. Numerische Mathematik, 129(4), 691–722. https://doi.org/10.1007/s00211-014-0651-z
Hoel, H., & Nyberg, H. (2014). An Extension of Clarke’s Model With Stochastic Amplitude Flip Processes. IEEE Transactions on Communications, 62(7), 2378–2389. https://doi.org/10.1109/tcomm.2014.2328595
Hoel, H., & Nyberg, H. (2014). An Extension of Clarke’s Model With Stochastic Amplitude Flip Processes. IEEE Transactions on Communications, 62(7), 2378–2389. https://doi.org/10.1109/tcomm.2014.2328595
Icardi, M., Ronco, G., Marchisio, D. L., & Labois, M. (2014). Efficient simulation of gas–liquid pipe flows using a generalized population balance equation coupled with the algebraic slip model. Applied Mathematical Modelling, 38(17–18), 4277–4290. https://doi.org/10.1016/j.apm.2014.04.052
Icardi, M., Ronco, G., Marchisio, D. L., & Labois, M. (2014). Efficient simulation of gas–liquid pipe flows using a generalized population balance equation coupled with the algebraic slip model. Applied Mathematical Modelling, 38(17–18), 4277–4290. https://doi.org/10.1016/j.apm.2014.04.052
Law, K. J. H., Neely, T. W., Kevrekidis, P. G., Anderson, B. P., Bradley, A. S., & Carretero-González, R. (2014). Dynamic and energetic stabilization of persistent currents in Bose-Einstein condensates. Physical Review A, 89(5). https://doi.org/10.1103/physreva.89.053606
Law, K. J. H., Neely, T. W., Kevrekidis, P. G., Anderson, B. P., Bradley, A. S., & Carretero-González, R. (2014). Dynamic and energetic stabilization of persistent currents in Bose-Einstein condensates. Physical Review A, 89(5). https://doi.org/10.1103/physreva.89.053606
Bauso, D., Dia, B. M., Djehiche, B., Tembine, H., & Tempone, R. (2014). Mean-Field Games for Marriage. PLoS ONE, 9(5), e94933. https://doi.org/10.1371/journal.pone.0094933
Bauso, D., Dia, B. M., Djehiche, B., Tembine, H., & Tempone, R. (2014). Mean-Field Games for Marriage. PLoS ONE, 9(5), e94933. https://doi.org/10.1371/journal.pone.0094933
Kiessling, J., & Tempone, R. (2014). Computable error estimates of a finite difference scheme for option pricing in exponential Lévy models. BIT Numerical Mathematics, 54(4), 1023–1065. https://doi.org/10.1007/s10543-014-0490-4
Kiessling, J., & Tempone, R. (2014). Computable error estimates of a finite difference scheme for option pricing in exponential Lévy models. BIT Numerical Mathematics, 54(4), 1023–1065. https://doi.org/10.1007/s10543-014-0490-4
Bayer, C., Hoel, H., von Schwerin, E., & Tempone, R. (2014). On NonAsymptotic Optimal Stopping Criteria in Monte Carlo Simulations. SIAM Journal on Scientific Computing, 36(2), A869–A885. https://doi.org/10.1137/130911433
Bayer, C., Hoel, H., von Schwerin, E., & Tempone, R. (2014). On NonAsymptotic Optimal Stopping Criteria in Monte Carlo Simulations. SIAM Journal on Scientific Computing, 36(2), A869–A885. https://doi.org/10.1137/130911433
Hoang, V. H., Law, K. J. H., & Stuart, A. M. (2014). Determining white noise forcing from Eulerian observations in the Navier-Stokes equation. Stochastic Partial Differential Equations: Analysis and Computations, 2(2), 233–261. https://doi.org/10.1007/s40072-014-0028-4
Hoang, V. H., Law, K. J. H., & Stuart, A. M. (2014). Determining white noise forcing from Eulerian observations in the Navier-Stokes equation. Stochastic Partial Differential Equations: Analysis and Computations, 2(2), 233–261. https://doi.org/10.1007/s40072-014-0028-4
Moraes, A., Tempone, R., & Vilanova, P. (2014). Hybrid Chernoff Tau-Leap. Multiscale Modeling & Simulation, 12(2), 581–615. https://doi.org/10.1137/130925657
Moraes, A., Tempone, R., & Vilanova, P. (2014). Hybrid Chernoff Tau-Leap. Multiscale Modeling & Simulation, 12(2), 581–615. https://doi.org/10.1137/130925657
Babuška, I., Motamed, M., & Tempone, R. (2014). A stochastic multiscale method for the elastodynamic wave equation arising from fiber composites. Computer Methods in Applied Mechanics and Engineering, 276, 190–211. https://doi.org/10.1016/j.cma.2014.02.018
Moraes, A., Ruggeri, F., Tempone, R., & Vilanova, P. (2014). Multiscale Modeling of Wear Degradation in Cylinder Liners. Multiscale Modeling & Simulation, 12(1), 396–409. https://doi.org/10.1137/130927024
Moraes, A., Ruggeri, F., Tempone, R., & Vilanova, P. (2014). Multiscale Modeling of Wear Degradation in Cylinder Liners. Multiscale Modeling & Simulation, 12(1), 396–409. https://doi.org/10.1137/130927024
Migliorati, G., Nobile, F., von Schwerin, E., & Tempone, R. (2014). Analysis of Discrete $$L^2$$ L 2 Projection on Polynomial Spaces with Random Evaluations. Foundations of Computational Mathematics. https://doi.org/10.1007/s10208-013-9186-4
Law, K. J. H., Sanz-Alonso, D., Shukla, A., & Stuart, A. M. (2014). Controlling Unpredictability with Observations in the Partially Observed Lorenz '96 Model. ArXiv. https://doi.org/10.48550/ARXIV.1411.3113

2013

Neely, T. W., Bradley, A. S., Samson, E. C., Rooney, S. J., Wright, E. M., Law, K. J. H., Carretero-González, R., Kevrekidis, P. G., Davis, M. J., & Anderson, B. P. (2013). Characteristics of Two-Dimensional Quantum Turbulence in a Compressible Superfluid. Physical Review Letters, 111(23). https://doi.org/10.1103/physrevlett.111.235301
Tembine, H., Zhu, Q., & Basar, T. (2014). Risk-Sensitive Mean-Field Games. IEEE Transactions on Automatic Control, 59(4), 835–850. https://doi.org/10.1109/tac.2013.2289711
Tembine, H., Zhu, Q., & Basar, T. (2014). Risk-Sensitive Mean-Field Games. IEEE Transactions on Automatic Control, 59(4), 835–850. https://doi.org/10.1109/tac.2013.2289711
Hoel, H., von Schwerin, E., Szepessy, A., & Tempone, R. (2014). Implementation and analysis of an adaptive multilevel Monte Carlo algorithm. Monte Carlo Methods and Applications, 20(1), 1–41. https://doi.org/10.1515/mcma-2013-0014
Litvinenko, A., Matthies, H. G., & El-Moselhy, T. A. (2013). Sampling and Low-Rank Tensor Approximation of the Response Surface. Monte Carlo and Quasi-Monte Carlo Methods 2012, 535–551. https://doi.org/10.1007/978-3-642-41095-6_27
Cirak, F., Long, Q., Bhattacharya, K., & Warner, M. (2014). Computational analysis of liquid crystalline elastomer membranes: Changing Gaussian curvature without stretch energy. International Journal of Solids and Structures, 51(1), 144–153. https://doi.org/10.1016/j.ijsolstr.2013.09.019
Cirak, F., Long, Q., Bhattacharya, K., & Warner, M. (2014). Computational analysis of liquid crystalline elastomer membranes: Changing Gaussian curvature without stretch energy. International Journal of Solids and Structures, 51(1), 144–153. https://doi.org/10.1016/j.ijsolstr.2013.09.019
Dashti, M., Law, K. J. H., Stuart, A. M., & Voss, J. (2013). MAP estimators and their consistency in Bayesian nonparametric inverse problems. Inverse Problems, 29(9), 095017. https://doi.org/10.1088/0266-5611/29/9/095017
Law, K., Shukla, A., & Stuart, A. (2014). Analysis of the 3DVAR filter for the partially observed Lorenz’63 model. Discrete & Continuous Dynamical Systems - A, 34(3), 1061–1078. https://doi.org/10.3934/dcds.2014.34.1061
Law, K., Shukla, A., & Stuart, A. (2014). Analysis of the 3DVAR filter for the partially observed Lorenz’63 model. Discrete & Continuous Dynamical Systems - A, 34(3), 1061–1078. https://doi.org/10.3934/dcds.2014.34.1061
Law, K. J. H. (2014). Proposals which speed up function-space MCMC. Journal of Computational and Applied Mathematics, 262, 127–138. https://doi.org/10.1016/j.cam.2013.07.026
Silva, A., Tembine, H., Altman, E., & Debbah, M. (2013). Optimum and Equilibrium in Assignment Problems With Congestion: Mobile Terminals Association to Base Stations. IEEE Transactions on Automatic Control, 58(8), 2018–2031. https://doi.org/10.1109/tac.2013.2250072
Iglesias, M. A., Law, K. J. H., & Stuart, A. M. (2013). Evaluation of Gaussian approximations for data assimilation in reservoir models. Computational Geosciences, 17(5), 851–885. https://doi.org/10.1007/s10596-013-9359-x
Blömker, D., Law, K., Stuart, A. M., & Zygalakis, K. C. (2013). Accuracy and stability of the continuous-time 3DVAR filter for the Navier–Stokes equation. Nonlinearity, 26(8), 2193–2219. https://doi.org/10.1088/0951-7715/26/8/2193
Migliorati, G., Nobile, F., von Schwerin, E., & Tempone, R. (2013). Approximation of Quantities of Interest in Stochastic PDEs by the Random Discrete $L^2$ Projection on Polynomial Spaces. SIAM Journal on Scientific Computing, 35(3), A1440–A1460. https://doi.org/10.1137/120897109
Beck, J., Nobile, F., Tamellini, L., & Tempone, R. (2014). Convergence of quasi-optimal Stochastic Galerkin methods for a class of PDES with random coefficients. Computers & Mathematics with Applications, 67(4), 732–751. https://doi.org/10.1016/j.camwa.2013.03.004
Nowak, W., & Litvinenko, A. (2013). Kriging and Spatial Design Accelerated by Orders of Magnitude: Combining Low-Rank Covariance Approximations with FFT-Techniques. Mathematical Geosciences, 45(4), 411–435. https://doi.org/10.1007/s11004-013-9453-6
Iglesias, M. A., Law, K. J. H., & Stuart, A. M. (2013). Ensemble Kalman methods for inverse problems. Inverse Problems, 29(4), 045001. https://doi.org/10.1088/0266-5611/29/4/045001
Long, Q., Scavino, M., Tempone, R., & Wang, S. (2013). Fast estimation of expected information gains for Bayesian experimental designs based on Laplace approximations. Computer Methods in Applied Mechanics and Engineering, 259, 24–39. https://doi.org/10.1016/j.cma.2013.02.017
Wang, C., Law, K. J. H., Kevrekidis, P. G., & Porter, M. A. (2013). Dark solitary waves in a class of collisionally inhomogeneous Bose-Einstein condensates. Physical Review A, 87(2). https://doi.org/10.1103/physreva.87.023621

2012

Björk, T., Szepessy, A., Tempone, R., & Zouraris, G. E. (2012). Monte Carlo Euler approximations of HJM term structure financial models. BIT Numerical Mathematics. https://doi.org/10.1007/s10543-012-0410-4
Espig, M., Hackbusch, W., Litvinenko, A., Matthies, H. G., & Wähnert, P. (2014). Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats. Computers & Mathematics with Applications, 67(4), 818–829. https://doi.org/10.1016/j.camwa.2012.10.008
Espig, M., Hackbusch, W., Litvinenko, A., Matthies, H. G., & Wähnert, P. (2014). Efficient low-rank approximation of the stochastic Galerkin matrix in tensor formats. Computers & Mathematics with Applications, 67(4), 818–829. https://doi.org/10.1016/j.camwa.2012.10.008
Motamed, M., Nobile, F., & Tempone, R. (2012). A stochastic collocation method for the second order wave equation with a discontinuous random speed. Numerische Mathematik, 123(3), 493–536. https://doi.org/10.1007/s00211-012-0493-5
Zhu, Q., Tembine, H., & Başar, T. (2012). Evolutionary Games for Multiple Access Control. Advances in Dynamic Games, 39–71. https://doi.org/10.1007/978-0-8176-8355-9_3
Beck, J., Tempone, R., Nobile, F., & Tamellini, L. (2012). On the optimal polynomial approximation of Stochastic PDEs by Galerkin and Collocation methods. Mathematical Models and Methods in Applied Sciences, 22(09), 1250023. https://doi.org/10.1142/s0218202512500236
Castrillón-Candás, J. E., Li, J., & Eijkhout, V. (2012). A discrete adapted hierarchical basis solver for radial basis function interpolation. BIT Numerical Mathematics, 53(1), 57–86. https://doi.org/10.1007/s10543-012-0397-x
Khan, M. A., Tembine, H., & Vasilakos, A. V. (2012). Evolutionary coalitional games: design and challenges in wireless networks. IEEE Wireless Communications, 19(2), 50–56. https://doi.org/10.1109/mwc.2012.6189413
Moon, K.-S., von Schwerin, E., Szepessy, A., & Tempone, R. (2005). An adaptive algorithm for ordinary, stochastic and partial differential equations. Contemporary Mathematics, 325–343. https://doi.org/10.1090/conm/383/07176
Khan, M. A., Tembine, H., & Vasilakos, A. V. (2012). Game Dynamics and Cost of Learning in Heterogeneous 4G Networks. IEEE Journal on Selected Areas in Communications, 30(1), 198–213. https://doi.org/10.1109/jsac.2012.120118

2011

Beck, J., Nobile, F., Tamellini, L., & Tempone, R. (2011). Implementation of optimal Galerkin and Collocation approximations of PDEs with Random Coefficients. ESAIM: Proceedings, 33, 10–21. https://doi.org/10.1051/proc/201133002
Babuška, I., Liu, K.-M., & Tempone, R. (2003). Solving Stochastic Partial Differential equations based on the Experimental Data. Mathematical Models and Methods in Applied Sciences, 13(03), 415–444. https://doi.org/10.1142/s021820250300257x
Karlsson, J., & Tempone, R. (2011). Towards automatic global error control: Computable weak error expansion for the tau-leap method. Monte Carlo Methods and Applications, 17(3). https://doi.org/10.1515/mcma.2011.011
Kiessling, J., & Tempone, R. (2011). Diffusion approximation of Lévy processes with a view towards finance. Monte Carlo Methods and Applications, 17(1). https://doi.org/10.1515/mcma.2011.003

2010

Motamed, M., Macdonald, C. B., & Ruuth, S. J. (2010). On the Linear Stability of the Fifth-Order WENO Discretization. Journal of Scientific Computing, 47(2), 127–149. https://doi.org/10.1007/s10915-010-9423-9
Bäck, J., Nobile, F., Tamellini, L., & Tempone, R. (2010). Stochastic Spectral Galerkin and Collocation Methods for PDEs with Random Coefficients: A Numerical Comparison. Spectral and High Order Methods for Partial Differential Equations, 43–62. https://doi.org/10.1007/978-3-642-15337-2_3
Babuška, I., Nobile, F., & Tempone, R. (2010). A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data. SIAM Review, 52(2), 317–355. https://doi.org/10.1137/100786356
Babuška, I., Nobile, F., & Tempone, R. (2010). A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data. SIAM Review, 52(2), 317–355. https://doi.org/10.1137/100786356
Bayer, C., Szepessy, A., & Tempone, R. (2010). Adaptive weak approximation of reflected and stopped diffusions. Monte Carlo Methods and Applications, 16(1), 1–67. https://doi.org/10.1515/mcma.2010.001
von Schwerin, E., & Szepessy, A. (2010). A stochastic phase-field model determined from molecular dynamics. ESAIM: Mathematical Modelling and Numerical Analysis, 44(4), 627–646. https://doi.org/10.1051/m2an/2010022
Motamed, M., & Runborg, O. (2010). Taylor expansion and discretization errors in Gaussian beam superposition. Wave Motion, 47(7), 421–439. https://doi.org/10.1016/j.wavemoti.2010.02.001

2009

Nobile, F., & Tempone, R. (2009). Analysis and implementation issues for the numerical approximation of parabolic equations with random coefficients. International Journal for Numerical Methods in Engineering, 80(6‒7), 979–1006. https://doi.org/10.1002/nme.2656

2008

Nobile, F., Tempone, R., & Webster, C. G. (2008). An Anisotropic Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM Journal on Numerical Analysis, 46(5), 2411–2442. https://doi.org/10.1137/070680540
Nobile, F., Tempone, R., & Webster, C. G. (2008). A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data. SIAM Journal on Numerical Analysis, 46(5), 2309–2345. https://doi.org/10.1137/060663660
Mordecki, E., Szepessy, A., Tempone, R., & Zouraris, G. E. (2008). Adaptive Weak Approximation of Diffusions with Jumps. SIAM Journal on Numerical Analysis, 46(4), 1732–1768. https://doi.org/10.1137/060669632
Babuška, I., Nobile, F., & Tempone, R. (2008). A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria. Computer Methods in Applied Mechanics and Engineering, 197(29–32), 2517–2539. https://doi.org/10.1016/j.cma.2007.08.031
Babuška, I., & Tempone, R. (2008). Static frame challenge problem: Summary. Computer Methods in Applied Mechanics and Engineering, 197(29–32), 2572–2577. https://doi.org/10.1016/j.cma.2007.09.030
Babuška, I., Nobile, F., & Tempone, R. (2008). Formulation of the static frame problem. Computer Methods in Applied Mechanics and Engineering, 197(29–32), 2496–2499. https://doi.org/10.1016/j.cma.2007.12.010

2007

Dowding, K. J., Red-Horse, J. R., Paez, T. L., Babuška, I. M., Hills, R. G., & Tempone, R. (2008). Validation challenge workshop summary. Computer Methods in Applied Mechanics and Engineering, 197(29–32), 2381–2384. https://doi.org/10.1016/j.cma.2007.10.015
Hills, R. G., Pilch, M., Dowding, K. J., Red-Horse, J., Paez, T. L., Babuška, I., & Tempone, R. (2008). Validation Challenge Workshop. Computer Methods in Applied Mechanics and Engineering, 197(29–32), 2375–2380. https://doi.org/10.1016/j.cma.2007.10.016
Babuška, I., Nobile, F., & Tempone, R. (2007). Reliability of computational science. Numerical Methods for Partial Differential Equations, 23(4), 753–784. https://doi.org/10.1002/num.20263

2006

Moon, K.-S., von Schwerin, E., Szepessy, A., & Tempone, R. (2006). Convergence Rates for an Adaptive Dual Weighted Residual Finite Element Algorithm. BIT Numerical Mathematics, 46(2), 367–407. https://doi.org/10.1007/s10543-006-0058-z

2005

Dzougoutov, A., Moon, K.-S., von Schwerin, E., Szepessy, A., & Tempone, R. (2005). Adaptive Monte Carlo Algorithms for Stopped Diffusion. Multiscale Methods in Science and Engineering, 59–88. https://doi.org/10.1007/3-540-26444-2_3
Moon, K.-S., Szepessy, A., Tempone, R., & Zouraris, G. E. (2005). Convergence Rates for Adaptive Weak Approximation of Stochastic Differential Equations. Stochastic Analysis and Applications, 23(3), 511–558. https://doi.org/10.1081/sap-200056678
Babuška, I., Nobile, F., & Tempone, R. (2005). Worst case scenario analysis for elliptic problems with uncertainty. Numerische Mathematik, 101(2), 185–219. https://doi.org/10.1007/s00211-005-0601-x

2004

Babuška, I., Tempone, R., & Zouraris, G. E. (2005). Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation. Computer Methods in Applied Mechanics and Engineering, 194(12–16), 1251–1294. https://doi.org/10.1016/j.cma.2004.02.026
Oden, J. T., Babuška, I., Nobile, F., Feng, Y., & Tempone, R. (2005). Theory and methodology for estimation and control of errors due to modeling, approximation, and uncertainty. Computer Methods in Applied Mechanics and Engineering, 194(2–5), 195–204. https://doi.org/10.1016/j.cma.2003.06.003
Babuska, I., Tempone, R., & Zouraris, G. E. (2004). Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations. SIAM Journal on Numerical Analysis, 42(2), 800–825. https://doi.org/10.1137/s0036142902418680
Moon, K.-S., Szepessy, A., Tempone, R., & Zouraris, G. E. (2003). A variational principle for adaptive approximation of ordinary differential equations. Numerische Mathematik, 96(1), 131–152. https://doi.org/10.1007/s00211-003-0467-8
Moon, K.-S., Szepessy, A., Tempone, R., & Zouraris, G. E. (2003). Convergence rates for adaptive approximation of ordinary differential equations. Numerische Mathematik, 96(1), 99–129. https://doi.org/10.1007/s00211-003-0466-9

2002

Gren, I.-M., Destouni, G., & Tempone, R. (2002). Cost effective policies for alternative distributions of stochastic water pollution. Journal of Environmental Management, 66(2), 145–157. https://doi.org/10.1006/jema.2002.0569

2001

Szepessy, A., Tempone, R., & Zouraris, G. E. (2001). Adaptive weak approximation of stochastic differential equations. Communications on Pure and Applied Mathematics, 54(10), 1169–1214. https://doi.org/10.1002/cpa.10000

1999

Accinelli, E., Piria, A., & Tempone, E. (1999). Optimizacion de Carteras de las Aseguradoras de Fondos de Retiro. Estudios Economicos, El Colegio de Mexico. 27, ene-jun 1999, Mexico. In Spanish