Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics

Viet Cuong, Dinh orcid logoORCID: 0009-0004-7841-4809, Lalić, Branislava, Petrić, Mina, Thanh Binh, Nguyen and Roantree, Mark orcid logoORCID: 0000-0002-1329-2570 (2024) Adapting physics-informed neural networks to improve ODE optimization in mosquito population dynamics. PLoS One, 19 (12). ISSN 1932-6203

Physics informed neural networks have been gaining popularity due to their unique ability to incorporate physics laws into data-driven models, ensuring that the predictions are not only consistent with empirical data but also align with domain-specific knowledge in the form of physics equations. The integration of physics principles enables the method to require less data while maintaining the robustness of deep learning in modelling complex dynamical systems. However, current PINN frameworks are not sufficiently mature for real-world ODE systems, especially those with extreme multi-scale behavior such as mosquito population dynamical modelling. In this research, we propose a PINN framework with several improvements for forward and inverse problems for ODE systems with a case study application in modelling the dynamics of mosquito populations. The framework tackles the gradient imbalance and stiff problems posed by mosquito ordinary differential equations. The method offers a simple but effective way to resolve the time causality issue in PINNs by gradually expanding the training time domain until it covers entire domain of interest. As part of a robust evaluation, we conduct experiments using simulated data to evaluate the effectiveness of the approach. Preliminary results indicate that physics-informed machine learning holds significant potential for advancing the study of ecological systems.
Item Type:Article (Published)
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Computer engineering
Computer Science > Computer software
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > INSIGHT Centre for Data Analytics
Publisher:Public Library of Science
Official URL:https://journals.plos.org/plosone/article?id=10.13...
Copyright Information:Authors
Funders:"This work was supported by Taighde Éireann – Research Ireland through the Insight Centre for Data Analytics (SFI/12/RC/2289\_P2) and by COST Action CA20108, supported by COST (European Cooperation in Science and Technology).The funders had no role in stu
ID Code:30650
Deposited On:10 Jan 2025 15:55 by Gordon Kennedy . Last Modified 10 Jan 2025 15:55

Full text available as:

[thumbnail of Adapting-physicsinformed-neural-networks-to-improve-ODE-optimization-in-mosquito-population-dynamicsPLoS-ONE.pdf]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Creative Commons: Attribution 4.0
2MB

Dimensions Badge

Downloads

Downloads per month over past year

Archive Staff Only: edit this record