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Stochastic Numerical Models of Oscillatory Phenomena

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Artificial Life and Evolutionary Computation (WIVACE 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 830))

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Abstract

The use of time series for integrating ordinary differential equations to model oscillatory chemical phenomena has shown benefits in terms of accuracy and stability. In this work, we suggest to adapt also the model in order to improve the matching of the numerical solution with the time series of experimental data. The resulting model is a system of stochastic differential equations. The stochastic nature depends on physical considerations and the noise relies on an arbitrary function which is empirically chosen. The integration is carried out through stochastic methods which integrate the deterministic part by using one-step methods and approximate the stochastic term by employing Monte Carlo simulations. Some numerical experiments will be provided to show the effectiveness of this approach.

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Author information

Authors and Affiliations

  1. Department of Engineering and Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy

    Raffaele D’Ambrosio

  2. Department of Mathematics, University of Salerno, Fisciano, Italy

    Martina Moccaldi & Beatrice Paternoster

  3. Department of Chemistry and Biology, University of Salerno, Fisciano, Italy

    Federico Rossi

Authors
  1. Raffaele D’Ambrosio
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  2. Martina Moccaldi
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  3. Beatrice Paternoster
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  4. Federico Rossi
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Corresponding author

Correspondence to Raffaele D’Ambrosio .

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  2. Ca’ Foscari University of Venice, Venice, Italy

    Irene Poli

  3. University of Bologna, Cesena, Italy

    Andrea Roli

  4. University of Modena and Reggio Emilia, Modena, Italy

    Roberto Serra

  5. Ca’ Foscari University of Venice, Venice, Italy

    Debora Slanzi

  6. University of Modena and Reggio Emilia, Modena, Italy

    Marco Villani

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D’Ambrosio, R., Moccaldi, M., Paternoster, B., Rossi, F. (2018). Stochastic Numerical Models of Oscillatory Phenomena. In: Pelillo, M., Poli, I., Roli, A., Serra, R., Slanzi, D., Villani, M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2017. Communications in Computer and Information Science, vol 830. Springer, Cham. https://doi.org/10.1007/978-3-319-78658-2_5

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  • DOI: https://doi.org/10.1007/978-3-319-78658-2_5

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  • Publisher Name: Springer, Cham

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