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

Chanaka Prabuddha Batuwatta Gamage

Published: Jan. 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.

Language: Английский

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

et al.

International Journal of Mechanical Sciences, Journal Year: 2024, Volume and Issue: unknown, P. 109766 - 109766

Published: Oct. 1, 2024

Language: Английский

Citations

5
An advanced physics-informed neural network-based framework for nonlinear and complex topology optimization DOI Creative Commons
Hyogu Jeong,

Chanaka Batuwatta-Gamage,

Jinshuai Bai

et al.

Engineering Structures, Journal Year: 2024, Volume and Issue: 322, P. 119194 - 119194

Published: Oct. 30, 2024

Language: Английский

Citations

5
Recent Advances in Food Drying Modeling: Empirical to Multiscale Physics‐Informed Neural Networks DOI

Aluth Durage Hiruni Tharaka Wijerathne,

Mohammad U. H. Joardder, Zachary G. Welsh

et al.

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2025, Volume and Issue: 24(3)

Published: May 1, 2025

ABSTRACT Food insecurity is a major global challenge. preservation, particularly through drying, presents promising solution to enhance food security and minimize waste. Fruits vegetables contain 80%–90% water, much of this removed during drying. However, structural changes across multiple length scales occur compromising stability affecting quality. Understanding these essential, several modeling techniques exist analyze them, including empirical modeling, physics‐based computational methods, purely data‐driven machine learning approaches, physics‐informed neural network (PINN) models. Although methods are straightforward implement, their limited generalizability lack physical insights have led the development methods. These can achieve high spatiotemporal resolution without requiring experimental investigations. complexity costs prompted exploration models for drying processes, which involve comparatively lower more execute. Nonetheless, poor predictive ability with sparse data has restricted application, leading hybrid approach: PINN, merges techniques. This method still holds significant potential advancements in modeling. Therefore, study aims conduct comprehensive literature review state‐of‐the‐art conventional techniques, such as empirical, computational, pure explores PINN approach overcoming limitations associated strategies.

Language: Английский

Citations

0
A two-step scaled physics-informed neural network for non-destructive testing of hull rib damage DOI
Xiaoqi Chen, Yongzhen Wang, Qinglei Zeng

et al.

Ocean Engineering, Journal Year: 2024, Volume and Issue: 319, P. 120260 - 120260

Published: Dec. 31, 2024

Language: Английский

Citations

1
A novel machine learning-based computational framework for predicting microscale morphological changes of plant cells during drying DOI Creative Commons

Chanaka Prabuddha Batuwatta Gamage

Published: Jan. 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.

Language: Английский

Citations

0