... Publishing Limited 2026 Emerald Publishing Limited Licensed re-use rights only Deep learning Physics informed neural networks Ternary hybrid nanofluid Inclined wavy surface Porous media Ministry of Education STARS/IISC/2023-0820 The authors acknowledge the Ministry of Education...

...” algorithm is introduced to improve the quality of particle distribution. Deep learning models for fluid flow applications typically use three primary network architectures: deep feedforward neural networks, convolutional neural networks, and graph neural networks (GNNs). When deep feedforward neural...

.../methodology/approach A two-step numerical analysis is used to develop and simulate a bioheat model using improved finite element method and deep learning algorithms, systematically regulating temperature distributions within the hydrogel artificial tissue during radiofrequency ablation (RFA). The model...

... analysis were reported and discussed. Deep learning Renewable energy Mathematical modeling Machine learning Fundação de Amparo à Pesquisa do Estado de Minas Gerais APQ-02427-21 a,b,c = Fitting parameters of equations (2) , (3) and (4) ; A,B = Fitting parameters...

...Armando Di Meglio; Nicola Massarotti; Perumal Nithiarasu Purpose In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based...

... Linear heat conduction Nonlinear heat conduction Deep learning Table 1. Architecture of DL models Inverse problem DNN architecture Linear conduction 4–64-32–16-4 Nonlinear conduction 4–64-32–16-4 Forced convection 4–64-32–16-16–16-4 Natural convection (10 × 10...