International Communications in Heat and Mass Transfer, Год журнала: 2025, Номер 165, С. 109059 - 109059
Опубликована: Май 14, 2025
Язык: Английский
International Communications in Heat and Mass Transfer, Год журнала: 2025, Номер 165, С. 109059 - 109059
Опубликована: Май 14, 2025
Язык: Английский
Journal of Hydrology Regional Studies, Год журнала: 2025, Номер 58, С. 102218 - 102218
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
1Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0Advances in Water Resources, Год журнала: 2025, Номер unknown, С. 104951 - 104951
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Physics of Fluids, Год журнала: 2025, Номер 37(4)
Опубликована: Апрель 1, 2025
This study provides an in-depth analysis of the combined effects spatially stationary surface waves, fluid inertia, and thermal radiation on natural convection along a heated vertical wavy surface. The is embedded within porous medium saturated with electrically conducting while also examining intricate dynamics magnetohydrodynamic (MHD) bioconvection. Conducted boundary layer regime at high Darcy–Rayleigh number, research employs coordinate transformation to reformulate governing equations into nonsimilar form. nonlinear coupled differential are solved using advanced framework deep learning, specifically through physics-informed neural networks (PINNs). investigates impact critical factors such as learning rates, neuron configurations, conditions, activation functions, number collocation training data points convergence rate accuracy PINNs. Additionally, it analyzes behavior loss function residuals various points. Comprehensive histograms generated illustrate dimensionless velocity, temperature, concentration, motile micro-organism density. Furthermore, evaluates influence key physical parameters local Nusselt Sherwood numbers, well density number. By leveraging techniques, this work valuable insights MHD bioconvection, demonstrating efficacy PINNs in modeling complex heat, mass, transfer phenomena media under radiation.
Язык: Английский
Процитировано
0Materials Today Communications, Год журнала: 2025, Номер unknown, С. 112491 - 112491
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Mathematics, Год журнала: 2025, Номер 13(10), С. 1571 - 1571
Опубликована: Май 10, 2025
Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bridge analysis remains under-explored. To address this gap, systematic review was conducted across Scopus and Web of Science (2020–2025), filtering records by relevance, journal impact, language. From an initial pool, 120 articles were selected categorised into nine thematic clusters that encompass computational frameworks, hybrid integration conventional solvers, domain decomposition strategies. Through natural language processing (NLP) trend mapping, evidences growing but fragmented research landscape. demonstrate promising capabilities load distribution modelling, structural health monitoring, failure prediction, particularly under dynamic train loads on multi-span bridges. However, methodological gaps persist large-scale simulations, plasticity experimental validation. Future work should focus scalable PINN architectures, refined modelling inelastic behaviours, real-time data assimilation, ensuring robustness generalisability through interdisciplinary collaboration.
Язык: Английский
Процитировано
0International Communications in Heat and Mass Transfer, Год журнала: 2025, Номер 165, С. 109059 - 109059
Опубликована: Май 14, 2025
Язык: Английский
Процитировано
0