A novel machine learning-based computational framework for predicting microscale morphological changes of plant cells during drying DOI Creative Commons

Chanaka Prabuddha Batuwatta Gamage

Опубликована: Янв. 1, 2024

This thesis proposes a novel and effective physics-informed machine learning framework to explore microscale variations in plant-based foods during drying. By initiating fundamental numerical investigations at the cellular level—which significantly influence bulk-level changes—this research highlights framework's robustness flexibility over traditional methods like FEA meshfree particle-based methods. The work demonstrates considerable potential for employing this learning-based computational modelling technique complex, nonlinear showcasing its superiority analysing predicting drying process of efficiently accurately.

Язык: Английский

Transfer Learning-Enhanced Finite Element-Integrated Neural Networks DOI Creative Commons
Ning Zhang, Kunpeng Xu, Zhen‐Yu Yin

и другие.

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110075 - 110075

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

3

Axial-offset magnetic negative stiffness spring with high density and linearity DOI
Ruiqi Gao, Jixing Che,

Mingkai Wu

и другие.

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 109989 - 109989

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Design and Kinematic Analysis of Origami Honeycomb Metamaterials with One-DOF Radial Motion DOI
Haojie Huang, Jinlong Jiang,

Yongquan Li

и другие.

Thin-Walled Structures, Год журнала: 2025, Номер unknown, С. 112978 - 112978

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Multistable bi-modulus Kresling origami with superior foldability and stiffness: An analytical design model DOI Creative Commons
Mingjin Cao, Li‐Qun Chen, Shaoyu Zhao

и другие.

Engineering Structures, Год журнала: 2025, Номер 330, С. 119955 - 119955

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

2

Energy absorption performance of Kresling origami tubes under impact loading DOI
Wei Qiang,

Haoxuan Feng,

Tuo Zhou

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 282, С. 109682 - 109682

Опубликована: Авг. 29, 2024

Язык: Английский

Процитировано

12

An inverse design framework for optimizing tensile strength of composite materials based on a CNN surrogate for the phase field fracture model DOI Creative Commons
Yuxiang Gao, Ravindra Duddu, Soheil Kolouri

и другие.

Composites Part A Applied Science and Manufacturing, Год журнала: 2025, Номер unknown, С. 108758 - 108758

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1

A Physics-Informed Neural Network framework to investigate nonlinear and heterogenous shrinkage of drying plant cells DOI Creative Commons

Chanaka Batuwatta-Gamage,

Charith Rathnayaka, H.C.P. Karunasena

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 275, С. 109267 - 109267

Опубликована: Апрель 7, 2024

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 simulate mass transfer; PINN-NS shrinkage. The coupled examine cellular changes resulting from moisture loss Firstly, the framework, in tandem with homogeneous conditions, operates parallel, allowing mutual parameters update between models. approach demonstrates ability approximate shrinkage within tissue, factoring influence surrounding cells. Secondly, non-uniform cell wall properties boundary conditions incorporated into this through domain decomposition. Inherent capabilities neural networks allow for seamless integration multiple domains, additional terms introduced at interfaces. shows capacity account drastic even under extreme drying which is key novelty has been challenging task existing traditional methods. Hence, proposed offers an innovative avenue understanding not only cells, but also soft matter general.

Язык: Английский

Процитировано

5

Physics-informed deep learning for structural dynamics under moving load DOI
Ruihua Liang, Weifeng Liu, Yuguang Fu

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер unknown, С. 109766 - 109766

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

5

Quantification of gradient energy coefficients using physics-informed neural networks DOI
Lan Shang, Yunhong Zhao, Sizheng Zheng

и другие.

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 273, С. 109210 - 109210

Опубликована: Март 25, 2024

Язык: Английский

Процитировано

4

Embedding Physics Information into Neural Networks to Enhance the Accuracy of Star-Shaped Elastic Metamaterial Design DOI Creative Commons
Jincheng He, Tao Chen, Gen Li

и другие.

Physics Letters A, Год журнала: 2025, Номер unknown, С. 130213 - 130213

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0