SiSyPHE: a python package for the simulation of systems of interacting mean-field particles with high efficiency
File(s)
Published version
Author(s)
Diez, Antoine
Type
Journal Article
Abstract
Over the past decades, the study of systems of particles has become an important part of many
research areas, from theoretical physics to applied biology and computational mathematics.
One of the main motivations in mathematical biology is the modelling of large animal societies
and the emergence of complex patterns from simple behavioral rules, e.g., flocks of birds, fish
schools, ant colonies, etc. In the microscopic world, particle systems are used to model a wide
range of phenomena, from the collective motion of spermatozoa to the anarchical development
of cancer cells. Within this perspective, there are at least three important reasons to conduct
large scale computer simulations of particle systems. First, numerical experiments are essential
to calibrate the models and test the influence of each parameter in a controlled environment.
For instance, the renowned Vicsek model (Vicsek et al., 1995) is a minimal model of flocking,
which exhibits a complex behavior, studied numerically in particular in (Chaté et al., 2008).
Secondly, particle simulations are used to check the validity of macroscopic models that
describe the statistical behavior of particle systems. These models are usually based on partial
differential equations (PDE) derived using phenomenological considerations that are often
difficult to justify mathematically (Degond et al., 2021; Degond & Motsch, 2008; Dimarco &
Motsch, 2016). Finally, inspired by models in biology, there is an ever growing literature on
the design of algorithms based on the simulation of artificial particle systems to solve tough
optimization problems (Grassi & Pareschi, 2020; Kennedy & Eberhart, 1995; Pinnau et al.,
2017; Totzeck, 2021) and to construct new more efficient Markov Chain Monte Carlo methods
(Cappé et al., 2004; Clarté et al., 2021; Del Moral, 1998, 2013; Doucet et al., 2001). The
simulation of systems of particles is also at the core of molecular dynamics (Leimkuhler &
Matthews, 2015), although the present library is not specifically written for this purpose. The
SiSyPHE library builds on recent advances in hardware and software for the efficient simulation
of large scale interacting mean-field particle systems, both on the GPU and on the CPU. The
versatile object-oriented Python interface of the library is designed for the simulation and
comparison of new and classical many-particle models of collective dynamics in mathematics
and active matter physics, enabling ambitious numerical experiments and leading to novel
conjectures and results.
research areas, from theoretical physics to applied biology and computational mathematics.
One of the main motivations in mathematical biology is the modelling of large animal societies
and the emergence of complex patterns from simple behavioral rules, e.g., flocks of birds, fish
schools, ant colonies, etc. In the microscopic world, particle systems are used to model a wide
range of phenomena, from the collective motion of spermatozoa to the anarchical development
of cancer cells. Within this perspective, there are at least three important reasons to conduct
large scale computer simulations of particle systems. First, numerical experiments are essential
to calibrate the models and test the influence of each parameter in a controlled environment.
For instance, the renowned Vicsek model (Vicsek et al., 1995) is a minimal model of flocking,
which exhibits a complex behavior, studied numerically in particular in (Chaté et al., 2008).
Secondly, particle simulations are used to check the validity of macroscopic models that
describe the statistical behavior of particle systems. These models are usually based on partial
differential equations (PDE) derived using phenomenological considerations that are often
difficult to justify mathematically (Degond et al., 2021; Degond & Motsch, 2008; Dimarco &
Motsch, 2016). Finally, inspired by models in biology, there is an ever growing literature on
the design of algorithms based on the simulation of artificial particle systems to solve tough
optimization problems (Grassi & Pareschi, 2020; Kennedy & Eberhart, 1995; Pinnau et al.,
2017; Totzeck, 2021) and to construct new more efficient Markov Chain Monte Carlo methods
(Cappé et al., 2004; Clarté et al., 2021; Del Moral, 1998, 2013; Doucet et al., 2001). The
simulation of systems of particles is also at the core of molecular dynamics (Leimkuhler &
Matthews, 2015), although the present library is not specifically written for this purpose. The
SiSyPHE library builds on recent advances in hardware and software for the efficient simulation
of large scale interacting mean-field particle systems, both on the GPU and on the CPU. The
versatile object-oriented Python interface of the library is designed for the simulation and
comparison of new and classical many-particle models of collective dynamics in mathematics
and active matter physics, enabling ambitious numerical experiments and leading to novel
conjectures and results.
Date Issued
2021-09-28
Date Acceptance
2021-09-01
Citation
Journal of Open Source Software, 2021, 6 (65), pp.1-4
ISSN
2475-9066
Publisher
Open Journals
Start Page
1
End Page
4
Journal / Book Title
Journal of Open Source Software
Volume
6
Issue
65
Copyright Statement
© 2021 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License.
License URL
Identifier
https://joss.theoj.org/papers/10.21105/joss.03653
Publication Status
Published
Date Publish Online
2021-09-28