Zobrazeno 1 - 10
of 1 667
pro vyhledávání: '"physics-informed neural networks"'
Publikováno v:
International Journal of Numerical Methods for Heat & Fluid Flow, 2024, Vol. 34, Issue 8, pp. 3131-3165.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/HFF-09-2023-0568
Publikováno v:
Journal of Rock Mechanics and Geotechnical Engineering, Vol 16, Iss 9, Pp 3812-3840 (2024)
In 2023, pivotal advancements in artificial intelligence (AI) have significantly experienced. With that in mind, traditional methodologies, notably the p-y approach, have struggled to accurately model the complex, nonlinear soil-structure interaction
Externí odkaz:
https://doaj.org/article/666d711e2e6743ff8705a8f3e9a91b9a
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Pedestrian two-stage crossings are common at large, busy signalized intersections with long crosswalks and high traffic volumes. This design aims to address pedestrian operation and safety by allowing navigation in two stages, negotiating ea
Externí odkaz:
https://doaj.org/article/26e84fb399b14f5c909cf77941d33e07
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Physics-informed neural networks (PINN) have recently become attractive for solving partial differential equations (PDEs) that describe physics laws. By including PDE-based loss functions, physics laws such as mass balance are enforced softl
Externí odkaz:
https://doaj.org/article/70aefee2c4c04c53a4971090dd5188f8
Publikováno v:
Human-Centric Intelligent Systems, Vol 4, Iss 3, Pp 335-343 (2024)
Abstract Machine intelligence is at great height these days and has been evident with its effective provenance in almost all domains of science and technology. This work will focus on one handy and profound application of machine intelligence-time se
Externí odkaz:
https://doaj.org/article/cc325282c7124efeb41121fec62edf24
Autor:
Lukács Kuslits, András Horváth, Viktor Wesztergom, Ciaran Beggan, Tibor Rubóczki, Ernő Prácser, Lili Czirok, István Bozsó, István Lemperger
Publikováno v:
Earth, Planets and Space, Vol 76, Iss 1, Pp 1-41 (2024)
Abstract Machine learning (ML) as a tool is rapidly emerging in various branches of contemporary geophysical research. To date, however, rarely has it been applied specifically for the study of Earth’s internal magnetic field and the geodynamo. Pre
Externí odkaz:
https://doaj.org/article/2a0ec0adb2f5479fbe9e21dafa708cc7
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 11, Iss 1, Pp 1-30 (2024)
Abstract This paper explores the ability of physics-informed neural networks (PINNs) to solve forward and inverse problems of contact mechanics for small deformation elasticity. We deploy PINNs in a mixed-variable formulation enhanced by output trans
Externí odkaz:
https://doaj.org/article/d6ec803a7f784131ba3caadd26db4e9f
Autor:
K. Chandan, Rania Saadeh, Ahmad Qazza, K. Karthik, R. S. Varun Kumar, R. Naveen Kumar, Umair Khan, Atef Masmoudi, M. Modather M. Abdou, Walter Ojok, Raman Kumar
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin struc
Externí odkaz:
https://doaj.org/article/924c317c0832419db93b2ce189caffd8
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-23 (2024)
Abstract Physics-informed neural networks (PINNs) are employed to solve the classical compressible flow problem in a converging–diverging nozzle. This problem represents a typical example described by the Euler equations, a thorough understanding o
Externí odkaz:
https://doaj.org/article/fe00cd8d476247fe84c3166634dee851
Publikováno v:
IEEE Access, Vol 12, Pp 115182-115196 (2024)
The high cost associated with high-fidelity computational fluid dynamics (CFD) is one of the main challenges that inhibit the design and optimisation of new fluid-flow systems. In this study, we explore the feasibility of a physics-informed deep lear
Externí odkaz:
https://doaj.org/article/ad8d5e549a3e45a1972bfba5a452adf3