pySODM : simulating and optimizing dynamical models in Python 3
- Author
- Tijs Alleman (UGent) , Christian Stevens (UGent) and Jan Baetens (UGent)
- Organization
- Project
- Abstract
- In this work we present our generic framework to construct, simulate and calibrate dynamical systems in Python 3. Its goal is to reduce the time it takes to implement a dynamical system with -dimensional states represented by coupled ordinary differential equations (ODEs), simulate the system deterministically or stochastically, and, calibrate the system using -dimensional data. We demonstrate our code’s capabilities by building three models in the context of two case studies. First, we forecast the yields of the enzymatic esterification reaction of D-glucose and lauric acid, performed in a continuous-flow, packed-bed reactor. The model yields a satisfactory description of the reaction yields under different flow rates and can be applied to design a viable process. Second, we build a stochastic, age-stratified model to make forecasts on the evolution of influenza in Belgium during the 2017–2018 season. Using only limited data, our simple model was able to make a fairly accurate assessment of the future course of the epidemic. By presenting real-world case studies from two scientific disciplines, we demonstrate our code’s applicability across domains.
- Keywords
- Modeling framework, Differential equations, Gillespie simulation, Markov Chain Monte Carlo sampling, Enzyme kinetics, Mathematical epidemiology
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01HCMD7A84A202Z7WP623ABPP1
- MLA
- Alleman, Tijs, et al. “PySODM : Simulating and Optimizing Dynamical Models in Python 3.” JOURNAL OF COMPUTATIONAL SCIENCE, vol. 73, 2023, doi:10.1016/j.jocs.2023.102148.
- APA
- Alleman, T., Stevens, C., & Baetens, J. (2023). pySODM : simulating and optimizing dynamical models in Python 3. JOURNAL OF COMPUTATIONAL SCIENCE, 73. https://doi.org/10.1016/j.jocs.2023.102148
- Chicago author-date
- Alleman, Tijs, Christian Stevens, and Jan Baetens. 2023. “PySODM : Simulating and Optimizing Dynamical Models in Python 3.” JOURNAL OF COMPUTATIONAL SCIENCE 73. https://doi.org/10.1016/j.jocs.2023.102148.
- Chicago author-date (all authors)
- Alleman, Tijs, Christian Stevens, and Jan Baetens. 2023. “PySODM : Simulating and Optimizing Dynamical Models in Python 3.” JOURNAL OF COMPUTATIONAL SCIENCE 73. doi:10.1016/j.jocs.2023.102148.
- Vancouver
- 1.Alleman T, Stevens C, Baetens J. pySODM : simulating and optimizing dynamical models in Python 3. JOURNAL OF COMPUTATIONAL SCIENCE. 2023;73.
- IEEE
- [1]T. Alleman, C. Stevens, and J. Baetens, “pySODM : simulating and optimizing dynamical models in Python 3,” JOURNAL OF COMPUTATIONAL SCIENCE, vol. 73, 2023.
@article{01HCMD7A84A202Z7WP623ABPP1,
abstract = {{In this work we present our generic framework to construct, simulate and calibrate dynamical systems in Python 3. Its goal is to reduce the time it takes to implement a dynamical system with -dimensional states represented by coupled ordinary differential equations (ODEs), simulate the system deterministically or stochastically, and, calibrate the system using -dimensional data. We demonstrate our code’s capabilities by building three models in the context of two case studies. First, we forecast the yields of the enzymatic esterification reaction of D-glucose and lauric acid, performed in a continuous-flow, packed-bed reactor. The model yields a satisfactory description of the reaction yields under different flow rates and can be applied to design a viable process. Second, we build a stochastic, age-stratified model to make forecasts on the evolution of influenza in Belgium during the 2017–2018 season. Using only limited data, our simple model was able to make a fairly accurate assessment of the future course of the epidemic. By presenting real-world case studies from two scientific disciplines, we demonstrate our code’s applicability across domains.}},
articleno = {{102148}},
author = {{Alleman, Tijs and Stevens, Christian and Baetens, Jan}},
issn = {{1877-7503}},
journal = {{JOURNAL OF COMPUTATIONAL SCIENCE}},
keywords = {{Modeling framework,Differential equations,Gillespie simulation,Markov Chain Monte Carlo sampling,Enzyme kinetics,Mathematical epidemiology}},
language = {{eng}},
pages = {{11}},
title = {{pySODM : simulating and optimizing dynamical models in Python 3}},
url = {{http://doi.org/10.1016/j.jocs.2023.102148}},
volume = {{73}},
year = {{2023}},
}
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