Toward intelligent food drying: Integrating artificial intelligence into drying systems

SH Miraei Ashtiani, A Martynenko - Drying Technology, 2024 - Taylor & Francis
Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly
evolving disciplines with increasing applications in modeling, simulation, control, and …

[HTML][HTML] A complete physics-informed neural network-based framework for structural topology optimization

H Jeong, C Batuwatta-Gamage, J Bai, YM **e… - Computer Methods in …, 2023 - Elsevier
Abstract Physics-Informed Neural Networks (PINNs) have recently gained increasing
attention in the field of topology optimization. The fusion of deep learning and topology …

A comprehensive review of heat pump wood drying technologies

L Gao, A Fix, T Seabourne, Y Pei, P Adegbaye… - Energy, 2024 - Elsevier
Wood drying is one of the most energy-intensive processes in lumber production, and most
wood drying kilns rely on combustion for the process heating. Heat pumps are a promising …

The application of physics-informed machine learning in multiphysics modeling in chemical engineering

Z Wu, H Wang, C He, B Zhang, T Xu… - Industrial & Engineering …, 2023 - ACS Publications
Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …

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

CP Batuwatta-Gamage, CM Rathnayaka… - International Journal of …, 2024 - Elsevier
This paper introduces a novel Physics-Informed Neural Network-based (PINN-based) multi-
domain computational framework to analyse nonlinear and heterogeneous morphological …

[HTML][HTML] An advanced physics-informed neural network-based framework for nonlinear and complex topology optimization

H Jeong, C Batuwatta-Gamage, J Bai… - Engineering …, 2025 - Elsevier
In this present paper, we introduce an advanced Physics-Informed Neural Network (PINN)
based Topology Optimization (TO) framework for addressing complex structural design …

[HTML][HTML] Recent advances in determining the cellular-level property evolutions of plant-based food materials during drying

VTW Thuppahige, ZG Welsh, M Joardder… - Trends in Food Science & …, 2024 - Elsevier
Background Determination of the changes in cellular-level structural, mechanical,
rheological and transport properties during the processing of plant-based food materials …

[HTML][HTML] Microwave-based and convective drying of cabbage (Brassica oleracea L. var capitata L.): computational intelligence modeling, thermophysical properties …

BS Luka, MJ Mactony, QM Vihikwagh… - Measurement: Food, 2024 - Elsevier
This study assessed and compared the impact of hot air oven drying (HAD) at 50, 60 and
70° C and microwave drying (MWD) at 195, 307 and 521 W on the kinetics of …

A data-physic driven method for gear fault diagnosis using PINN and pseudo-dynamic features

Y Yang, X Wang, J Li, R Ge - Measurement, 2024 - Elsevier
Gear transmissions are extensively applied in automobiles, rail vehicles, watercraft, aircraft,
and so on. As the core component, gears directly affect the integral performance and …

A meshless Runge-Kutta-based Physics-Informed Neural Network framework for structural vibration analysis

S **ao, J Bai, H Jeong, L Alzubaidi, YT Gu - Engineering Analysis with …, 2025 - Elsevier
Abstract In recent years, Physics-Informed Neural Networks (PINN) have emerged as
powerful meshless numerical methods for solving partial differential equations (PDEs) in …