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Title: Stochastic self-tuning hybrid algorithm for reaction-diffusion systems

Authors:
 [1];  [2];  [2]; ORCiD logo [3]
  1. Department of Mechanical and Aerospace Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
  2. Computational Neurobiology Laboratory, Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, USA
  3. Department of Energy Resources Engineering, Stanford University, 367 Panama Street, Stanford, California 94305, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1580549
Grant/Contract Number:  
SC0019130
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Name: Journal of Chemical Physics Journal Volume: 151 Journal Issue: 24; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

Citation Formats

Ruiz-Martínez, Á., Bartol, T. M., Sejnowski, T. J., and Tartakovsky, D. M. Stochastic self-tuning hybrid algorithm for reaction-diffusion systems. United States: N. p., 2019. Web. doi:10.1063/1.5125022.
Ruiz-Martínez, Á., Bartol, T. M., Sejnowski, T. J., & Tartakovsky, D. M. Stochastic self-tuning hybrid algorithm for reaction-diffusion systems. United States. doi:10.1063/1.5125022.
Ruiz-Martínez, Á., Bartol, T. M., Sejnowski, T. J., and Tartakovsky, D. M. Sat . "Stochastic self-tuning hybrid algorithm for reaction-diffusion systems". United States. doi:10.1063/1.5125022.
@article{osti_1580549,
title = {Stochastic self-tuning hybrid algorithm for reaction-diffusion systems},
author = {Ruiz-Martínez, Á. and Bartol, T. M. and Sejnowski, T. J. and Tartakovsky, D. M.},
abstractNote = {},
doi = {10.1063/1.5125022},
journal = {Journal of Chemical Physics},
number = 24,
volume = 151,
place = {United States},
year = {2019},
month = {12}
}

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