Validation data for a hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) method

Brian Drawert; Bruno Jacob; Zhen Li; Tau-Mu Yi; Linda Petzold

doi:10.1016/j.dib.2018.11.103

Validation data for a hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) method

Data Brief. 2018 Dec 5:22:11-15. doi: 10.1016/j.dib.2018.11.103. eCollection 2019 Feb.

Authors

Brian Drawert¹, Bruno Jacob², Zhen Li³, Tau-Mu Yi⁴, Linda Petzold^{2

5}

Affiliations

¹ Department of Computer Science, University of North Carolina at Asheville, Asheville, NC 28804, USA.
² Department of Mechanical Engineering, University of California - Santa Barbara, Santa Barbara, CA 93106, USA.
³ Division of Applied Mathematics, Brown University, Providence, RI 02912, USA.
⁴ Department of Molecular, Cellular, and Developmental Biology, University of California - Santa Barbara, Santa Barbara, CA 93106, USA.
⁵ Department of Computer Science, University of California - Santa Barbara, Santa Barbara, CA 93106, USA.

Abstract

We present the validation of the hybrid sSSA-SDPD method for advection-diffusion-reaction problems coupled to discrete biochemical systems, as presented in the publication "A hybrid smoothed dissipative particle dynamics (SDPD) spatial stochastic simulation algorithm (sSSA) for advection-diffusion-reaction problems" (Drawert et al., 2019). We validate 1D diffusion, and 2D diffusion cases against their analytical solutions. We present graphs and tables of data showing the error in the simulation method.

Abstract

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