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2020 – today
- 2024
[i7]Guglielmo Gattiglio, Lyudmila Grigoryeva, Massimiliano Tamborrino:
Nearest Neighbors GParareal: Improving Scalability of Gaussian Processes for Parallel-in-Time Solvers. CoRR abs/2405.12182 (2024)- 2023
- [j10]Kamran Pentland
, Massimiliano Tamborrino, Timothy John Sullivan, James Buchanan, Lynton C. Appel:
GParareal: a time-parallel ODE solver using Gaussian process emulation. Stat. Comput. 33(1): 23 (2023) - [j9]Kamran Pentland, Massimiliano Tamborrino, Debasmita Samaddar, Lynton C. Appel:
Stochastic Parareal: An Application of Probabilistic Methods to Time-Parallelization. SIAM J. Sci. Comput. 45(3): S82-S102 (2023) - [j8]Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan:
Error Bound Analysis of the Stochastic Parareal Algorithm. SIAM J. Sci. Comput. 45(5): 2657- (2023) - [i6]Susanne Ditlevsen, Massimiliano Tamborrino, Irene Tubikanec:
Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation. CoRR abs/2306.15787 (2023) - 2022
- [j7]Irene Tubikanec, Massimiliano Tamborrino, Petr Lánský, Evelyn Buckwar:
Qualitative properties of different numerical methods for the inhomogeneous geometric Brownian motion. J. Comput. Appl. Math. 406: 113951 (2022) - [i5]Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan, James Buchanan, Lynton C. Appel:
GParareal: A time-parallel ODE solver using Gaussian process emulation. CoRR abs/2201.13418 (2022) - [i4]Kamran Pentland, Massimiliano Tamborrino, Timothy John Sullivan:
Error bound analysis of the stochastic parareal algorithm. CoRR abs/2211.05496 (2022) - 2021
- [j6]Matt J. Keeling, Edward M. Hill, Erin E. Gorsich, Bridget S. Penman, Glen Guyver-Fletcher, Alex Holmes, Trystan Leng, Hector McKimm, Massimiliano Tamborrino, Louise Dyson, Michael J. Tildesley:
Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies. PLoS Comput. Biol. 17(1) (2021) - [i3]Evelyn Buckwar, Adeline Samson, Massimiliano Tamborrino, Irene Tubikanec:
Splitting methods for SDEs with locally Lipschitz drift. An illustration on the FitzHugh-Nagumo model. CoRR abs/2101.01027 (2021) - [i2]Kamran Pentland, Massimiliano Tamborrino, D. Samaddar, Lynton C. Appel:
Stochastic parareal: an application of probabilistic methods to time-parallelisation. CoRR abs/2106.10139 (2021) - 2020
- [j5]Evelyn Buckwar, Massimiliano Tamborrino, Irene Tubikanec:
Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs. Stat. Comput. 30(3): 627-648 (2020) - [i1]Irene Tubikanec, Massimiliano Tamborrino, Petr Lánský, Evelyn Buckwar:
Qualitative properties of numerical methods for the inhomogeneous geometric Brownian motion. CoRR abs/2003.10193 (2020)
2010 – 2019
- 2016
- [j4]Laura Sacerdote, Massimiliano Tamborrino, Cristina Zucca:
First passage times of two-dimensional correlated processes: Analytical results for the Wiener process and a numerical method for diffusion processes. J. Comput. Appl. Math. 296: 275-292 (2016) - [j3]Marie Levakova, Massimiliano Tamborrino, Lubomir Kostal, Petr Lánský:
Presynaptic Spontaneous Activity Enhances the Accuracy of Latency Coding. Neural Comput. 28(10): 2162-2180 (2016) - 2015
- [j2]Marie Levakova, Massimiliano Tamborrino, Susanne Ditlevsen, Petr Lánský:
A review of the methods for neuronal response latency estimation. Biosyst. 136: 23-34 (2015) - 2013
- [j1]Massimiliano Tamborrino, Susanne Ditlevsen, Petr Lánský:
Parametric inference of neuronal response latency in presence of a background signal. Biosyst. 112(3): 249-257 (2013)
Coauthor Index
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