A physics-informed neural network framework to investigate nonlinear and heterogenous shrinkage of drying plant cells

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 PINN-NS 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.