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
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
Bartuska, A., Espath, L., & Tempone, R. (2023). Laplace-based strategies for Bayesian optimal experimental design with nuisance uncertainty (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2310.10783
Chaabane, K. B., Kebaier, A., Scavino, M., & Tempone, R. (2023). Data-driven uncertainty quantification for constrained stochastic differential equations and application to solar photovoltaic power forecast data (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2302.13133
Terhag, F., Knechtges, P., Basermann, A., & Tempone, R. (2023). Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2312.02167
Liu, Y., & Tempone, R. (2023). Nonasymptotic Convergence Rate of Quasi-Monte Carlo: Applications to Linear Elliptic PDEs with Lognormal Coefficients and Importance Samplings (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2310.14351
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
Davis, O., Motamed, M., & Tempone, R. (2023). Residual Multi-Fidelity Neural Network Computing (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2310.03572
Hammouda, C. B., Rezvanova, E., von Schwerin, E., & Tempone, R. (2023). Lagrangian Relaxation for Continuous-Time Optimal Control of Coupled Hydrothermal Power Systems Including Storage Capacity and a Cascade of Hydropower Systems with Time Delays (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2311.00794

2022

Bayer, C., Hammouda, C. B., Papapantoleon, A., Samet, M., & Tempone, R. (2022). Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models. arXiv preprint, ArXiv: 2203.08196
Amar, E. B., Rached, N. B., Haji-Ali, A.-L., & Tempone, R. (2022). Efficient Importance Sampling Algorithm Applied to the Performance Analysis of Wireless Communication Systems Estimation. arXiv preprint, ArXiv: 2201.01240
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
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
Carlon, A., Espath, L., & Tempone, R. (2022). Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization. arXiv. https://doi.org/10.48550/ARXIV.2208.00441
Amar, E. B., Rached, N. B., Haji-Ali, A.-L., & Tempone, R. (2022). State-dependent Importance Sampling for Estimating Expectations of Functionals of Sums of Independent Random Variables. arXiv. https://doi.org/10.48550/ARXIV.2201.01340
Rached, N. B., Haji-Ali, A.-L., Pillai, S. M. S., & Tempone, R. (2022). Single Level Importance Sampling for McKean-Vlasov Stochastic Differential Equation (Version 4). arXiv. https://doi.org/10.48550/ARXIV.2207.06926
Cramer, E., Rauh, F., Mitsos, A., Tempone, R., & Dahmen, M. (2022). Nonlinear Isometric Manifold Learning for Injective Normalizing Flows (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2203.03934
Rached, N. B., Haji-Ali, A.-L., Pillai, S. M. S., & Tempone, R. (2022). Multilevel Importance Sampling for McKean-Vlasov Stochastic Differential Equation. arXiv. https://doi.org/10.48550/ARXIV.2208.03225
Bayer, C., Hammouda, C. B., Papapantoleon, A., Samet, M., & Tempone, R. (2022). Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2203.08196
Beck, J., Liu, Y., von Schwerin, E., & Tempone, R. (2022). Goal-Oriented Adaptive Finite Element Multilevel Monte Carlo with Convergence Rates (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2206.10314

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
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
Espath, L., Krumscheid, S., Tempone, R., & Vilanova, P. (2021). On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods. arXiv. https://doi.org/10.48550/ARXIV.2109.10933
Hoang, T.-V., Krumscheid, S., Matthies, H. G., & Tempone, R. (2021). Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2106.07908
Bayer, C., Hammouda, C. B., & Tempone, R. (2021). Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing. arXiv. https://doi.org/10.48550/ARXIV.2111.01874
Madrigal-Cianci, J. P., Nobile, F., & Tempone, R. (2021). Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2105.02035
Hoel, H., Shaimerdenova, G., & Tempone, R. (2021). Multi-index ensemble Kalman filtering. ArXiv. https://doi.org/10.48550/ARXIV.2104.07263
Hammouda, C. B., Rached, N. B., Tempone, R., & Wiechert, S. (2021). Learning-Based Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks. arXiv. https://doi.org/10.48550/ARXIV.2110.14335
Hammouda, C. B., Rached, N. B., Tempone, R., & Wiechert, S. (2021). Learning-Based Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks (Version 4). arXiv. https://doi.org/10.48550/ARXIV.2110.14335
Bartuska, A., Espath, L., & Tempone, R. (2021). Small-noise approximation for Bayesian optimal experimental design with nuisance uncertainty. ArXiv. https://doi.org/10.48550/ARXIV.2112.06794
Haji-Ali, A.-L., Hoel, H., & Tempone, R. (2021). A simple approach to proving the existence, uniqueness, and strong and weak convergence rates for a broad class of McKean--Vlasov equations. arXiv. https://doi.org/10.48550/ARXIV.2101.00886

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
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
Carlon, A., Espath, L., Lopez, R., & Tempone, R. (2020). Multi-Iteration Stochastic Optimizers. arXiv. https://doi.org/10.48550/ARXIV.2011.01718
Bayer, C., Hammouda, C. B., & Tempone, R. (2020). Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation. arXiv. https://doi.org/10.48550/ARXIV.2003.05708
Bayer, C., Hall, E. J., & Tempone, R. (2020). Weak error rates for option pricing under linear rough volatility. arXiv. https://doi.org/10.48550/ARXIV.2009.01219
Caballero, R., Kebaier, A., Scavino, M., & Tempone, R. (2020). Quantifying Uncertainty with a Derivative Tracking SDE Model and Application to Wind Power Forecast Data (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2006.15907
Kammonen, A., Kiessling, J., Plecháč, P., Sandberg, M., Szepessy, A., & Tempone, R. (2020). Smaller generalization error derived for a deep residual neural network compared to shallow networks (Version 2). arXiv. https://doi.org/10.48550/ARXIV.2010.01887

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
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
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
Issaid, C. B., Alouini, M.-S., & Tempone, R. (2019). Eficient Monte Carlo Simulation of the Left Tail of Positive Gaussian Quadratic Forms. arXiv. https://doi.org/10.48550/ARXIV.1901.09174
Hammouda, C. B., Rached, N. B., & Tempone, R. (2019). Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks. ArXiv. https://doi.org/10.48550/ARXIV.1911.06286
Rached, N. B., Kammoun, A., Alouini, M.-S., & Tempone, R. (2019). An Accurate Sample Rejection Estimator for the Estimation of Outage Probability of EGC Receivers. arXiv. https://doi.org/10.48550/ARXIV.1903.05481

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
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., 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
Bayer, C., Hammouda, C. B., & Tempone, R. (2018). Hierarchical adaptive sparse grids and quasi Monte Carlo for option pricing under the rough Bergomi model. ArXiv. https://doi.org/10.48550/ARXIV.1812.08533
Bayer, C., Tempone, R., & Wolfers, S. (2018). Pricing American Options by Exercise Rate Optimization. arXiv. https://doi.org/10.48550/ARXIV.1809.07300
Beck, J., Dia, B. M., Espath, L. F. R., & Tempone, R. (2018). Multilevel Double Loop Monte Carlo and Stochastic Collocation Methods with Importance Sampling for Bayesian Optimal Experimental Design. arXiv. https://doi.org/10.48550/ARXIV.1811.11469
Litvinenko, A., Yucel, A. C., Bagci, H., Oppelstrup, J., Michielssen, E., & Tempone, R. (2018). Computation of Electromagnetic Fields Scattered From Objects With Uncertain Shapes Using Multilevel Monte Carlo Method. arXiv. https://doi.org/10.48550/ARXIV.1809.00362
Carlon, A. G., Dia, B. M., Espath, L. F. R., Lopez, R. H., & Tempone, R. (2018). Nesterov-aided Stochastic Gradient Methods using Laplace Approximation for Bayesian Design Optimization. arXiv. https://doi.org/10.48550/ARXIV.1807.00653
Beck, J., Tamellini, L., & Tempone, R. (2018). IGA-based Multi-Index Stochastic Collocation for random PDEs on arbitrary domains. ArXiv. https://doi.org/10.48550/ARXIV.1810.01661

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
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
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
Haji-Ali, A.-L., Nobile, F., Tempone, R., & Wolfers, S. (2017). Multilevel weighted least squares polynomial approximation. arXiv. https://doi.org/10.48550/ARXIV.1707.00026
Chernov, A., Hoel, H., Law, K. J. H., Nobile, F., & Tempone, R. (2017). Multilevel ensemble Kalman filtering for spatio-temporal processes. arXiv. https://doi.org/10.48550/ARXIV.1710.07282
Alouini, M.-S., Rached, N. B., Kammoun, A., & Tempone, R. (2017). On the Efficient Simulation of the Left-Tail of the Sum of Correlated Log-normal Variates. arXiv. https://doi.org/10.48550/ARXIV.1705.07635
Tempone, R., & Wolfers, S. (2017). Smolyak's algorithm: A powerful black box for the acceleration of scientific computations. arXiv. https://doi.org/10.48550/ARXIV.1703.08872
Bayer, C., Häppölä, J., & Tempone, R. (2017). Implied Stopping Rules for American Basket Options from Markovian Projection. arXiv. https://doi.org/10.48550/ARXIV.1705.00558

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
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
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
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
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
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
Nobile, F., Tempone, R., & Wolfers, S. (2016). Sparse approximation of multilinear problems with applications to kernel-based methods in UQ. arXiv. https://doi.org/10.48550/ARXIV.1609.00246
Bayer, C., Siebenmorgen, M., & Tempone, R. (2016). Smoothing the payoff for efficient computation of Basket option prices. arXiv. https://doi.org/10.48550/ARXIV.1607.05572

2015

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
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
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
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
Hall, E. J., Hoel, H., Sandberg, M., Szepessy, A., & Tempone, R. (2015). Computable error estimates for finite element approximations of elliptic partial differential equations with rough stochastic data. ArXiv. https://doi.org/10.48550/ARXIV.1510.02708
Beskos, A., Jasra, A., Law, K., Tempone, R., & Zhou, Y. (2015). Multilevel Sequential Monte Carlo Samplers. ArXiv. https://doi.org/10.48550/ARXIV.1503.07259

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
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
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
Tembine, H., Tempone, R., & Vilanova, P. (2012). Mean field games for cognitive radio networks. 2012 American Control Conference (ACC). https://doi.org/10.1109/acc.2012.6314643
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
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
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
Tembine, H., Tempone, R., & Vilanova, P. (2013). Mean-field learning for satisfactory solutions. 52nd IEEE Conference on Decision and Control. https://doi.org/10.1109/cdc.2013.6760653
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
Hoel, H., Häppölä, J., & Tempone, R. (2014). Construction of a Mean Square Error Adaptive Euler--Maruyama Method with Applications in Multilevel Monte Carlo. arXiv. https://doi.org/10.48550/ARXIV.1411.5515
Rached, N. B., Benkhelifa, F., Kammoun, A., Alouini, M.-S., & Tempone, R. (2014). A Fast Simulation Method for the Sum of Subexponential Distributions. arXiv. https://doi.org/10.48550/ARXIV.1406.4689
Law, K. J. H., Tembine, H., & Tempone, R. (2014). Deterministic Mean-field Ensemble Kalman Filtering. ArXiv. https://doi.org/10.48550/ARXIV.1409.0628

2013

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
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
Long, Q., Scavino, M., Tempone R., & Wang, S. (2013) A projection method for under determined optimal experimental designs.11th International Conference On Structural Safety & Reliability, Columbia University, New York, USA, 6-20th June 2013
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
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
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

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
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
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
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
Tembine, H., Tempone, R., & Vilanova, P. (2011). Mean field interaction in biochemical reaction networks. 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton). https://doi.org/10.1109/allerton.2011.6120274
Tembine, H., Tempone, R., & Vilanova, P. (2012). Mean-Field Learning: a Survey. arXiv. https://doi.org/10.48550/ARXIV.1210.4657

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
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
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
Bayer, C., Hoel, H., Plecháč, P., Szepessy, A., & Tempone, R. (2011). How accurate is molecular dynamics?. arXiv. https://doi.org/10.48550/ARXIV.1104.0953

2010

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

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
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
Babuska, I., Nobile, F., & Tempone, R. (2005). Worst-case scenario analysis for elliptic PDE’s with uncertainty. 6th European Conference on Structural Dynamics, EURODYN, Paris, France, 4-7th September 2005
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). Convergence rates for adaptive approximation of ordinary differential equations. Numerische Mathematik, 96(1), 99–129. https://doi.org/10.1007/s00211-003-0466-9
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

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

1998

Oppelstrup, J., & Tempone, R. (1998). On approximation-interpolation of incompressible flows. 4th World Congress of Computational Mechanics (WCCM), Buenos Aires, Argentina, July 1998.