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Published VersionCC BY V4.0, Open Access
Journal article
A physics-informed neural network framework to investigate nonlinear and heterogenous shrinkage of drying plant cells
International Journal of Mechanical Sciences, Vol.275, pp.1-20
2024
Abstract
This paper introduces a novel Physics-Informed Neural Network-based (PINN-based) multi-domain computational framework to analyse nonlinear and heterogeneous morphological variations of plant cells during drying. Here, two distinct models are involved: PINN-MT to simulate mass transfer; and PINNNS to simulate nonlinear shrinkage. The models are coupled to examine cellular morphological changes resulting from moisture loss during drying. Firstly, the coupled framework, in tandem with homogeneous conditions, operates in parallel, allowing the mutual parameters to update between models. This approach demonstrates ability to approximate homogeneous cellular shrinkage within a tissue, factoring in the influence of surrounding plant cells. Secondly, non-uniform cell wall properties and heterogeneous boundary conditions are incorporated into this computational framework through domain decomposition. Inherent capabilities of neural networks allow for seamless integration of multiple domains, with additional loss terms introduced at interfaces. The framework shows capacity to account for drastic and non-uniform morphological variations of plant cells even under extreme drying conditions, which is the key novelty and has been a challenging task for existing traditional computational methods. Hence, the proposed computational approach offers an innovative avenue for understanding nonlinear and heterogeneous morphological variations not only for plant cells, but also for soft matter in general.
Details
- Title
- A physics-informed neural network framework to investigate nonlinear and heterogenous shrinkage of drying plant cells
- Authors
- C.P. Batuwatta-Gamage (Corresponding Author) - Queensland University of TechnologyC.M. Rathnayaka (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringH.C.P. Karunasena - University of RuhunaH. Jeong - Queensland University of TechnologyM.A. Karim - Queensland University of TechnologyY.T. Gu - Queensland University of Technology
- Publication details
- International Journal of Mechanical Sciences, Vol.275, pp.1-20
- Publisher
- Elsevier Ltd
- Date published
- 2024
- DOI
- 10.1016/j.ijmecsci.2024.109267
- ISSN
- 1879-2162; 0020-7403
- Copyright note
- © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
- Data Availability
- No data was used for the research described in the article.
- Grants
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991031598902621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Engineering, Mechanical
- Mechanics