Results 211 to 220 of about 2,349 (245)
Evolutionary Digital Twin for Oil and Gas Pipelines: A Cognitive Multi-Agent Framework with Continuous Feedback Learning. [PDF]
Sensors (Basel)Shi N +7 more
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NAS-PINN: Neural architecture search-guided physics-informed neural network for solving PDEs
Journal of Computational Physics, 2023 19 pages, 14 figures, 5 ...
Linlin Zhong
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NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems
Journal of Computational Physics, 2022 Physics-informed neural network (PINN) is a data-driven approach to solve equations. It is successful in many applications; however, the accuracy of the PINN is not satisfactory when it is used to solve multiscale equations. Homogenization is a way of approximating a multiscale equation by a homogenized equation without multiscale property; it includes
Wing Tat Leung +2 more
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Computer Methods in Applied Mechanics and Engineering, 2023 The first-order reliability method (FORM) is commonly used in the field of structural reliability analysis, which transforms the reliability analysis problem into the solution of an optimization problem with equality constraint.
Zeng Meng +2 more
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A data‐assisted physics‐informed neural network (DA‐PINN) for fretting fatigue lifetime prediction
International Journal of Mechanical System DynamicsIn this study, we present for the first time the application of physics-informed neural network (PINN) to fretting fatigue problems. Although PINN has recently been applied to pure fatigue lifetime prediction, it has not yet been explored in the case of ...
Zhikun Zhou, Magd Abdel Wahab
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Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems
Computer Methods in Applied Mechanics and EngineeringzbMATH Open Web Interface contents unavailable due to conflicting licenses.
Antareep Kumar Sarma +4 more
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Physics-Informed Neural Networks (PINNs) in Finance
SSRN Electronic Journal, 2023Miquel Noguer i Alonso, Daniel Maxwell
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Physics-Informed Neural Networks (PINNs) for Axisymmetric Nanoplates
Elastostatics of axisymmetric Kirchhoff nanoplates is investigated exploiting a stress-drivennonlocal theory to capture size-dependent mechanical behaviours. Physics-Informed NeuralNetworks (PINNs) are applied as a cutting-edge machine learning tool to solve the governingsixth-order differential problem, offering a powerful alternative to traditional ...Baidehi Das +3 more
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