Results 31 to 40 of about 8,084,029 (292)
fPINNs: Fractional Physics-Informed Neural Networks [PDF]
SIAM Journal on Scientific Computing, 2019 Physics-informed neural networks (PINNs), introduced in [M. Raissi, P. Perdikaris, and G. Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707], are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data.
Guofei Pang +2 more
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Dynamic System Discovery with Recursive Physics-Informed Neural Networks [PDF]
, 2022 This thesis presents a novel method, recursive Physics informed neural network, to learn the right hand side of differential equations. The neural network takes in data, then trains, and then acts as a proxy for the differential equation which can be ...
Mau, Jarrod
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IET Electric Power Applications, 2022 Convolutional neural networks (CNN) have shown great potentials and have been proven to be an effective tool for some image‐based deep learning tasks in the field of computational electromagnetism (CEM).
Ruohan Gong, Zuqi Tang
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Retrieval of Elastic Constants of Liquid Crystals Using Physics-Informed Neural Network
, 2023 A novel approach to retrieve the elastic constants of a nematic liquid crystal based on physics-informed deep neural network is ...
Jisha, Chandroth P. +2 more
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On physics-informed neural networks for quantum computers
Frontiers in Applied Mathematics and Statistics, 2022 Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks. One of the advantages of using PINN is to leverage the usage of Machine Learning computational frameworks relying on the combined usage of CPUs and co ...
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Physics informed neural networks for continuum micromechanics [PDF]
Computer Methods in Applied Mechanics and Engineering, 2021 AbstractThe present work proposes a Physics Informed Neural Network (PINN) for solving boundary value problems in continuum micromechanics. The presented technique is therefore an alternative to the finite element method or Fourier transform based methods.
Alexander Henkes +2 more
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Scientific Reports, 2023 Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge ...
Zhonghai Lu +3 more
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Discontinuity Computing Using Physics-Informed Neural Networks
Journal of Scientific Computing, 2022 Simulating discontinuities is a long standing problem particularly for shock waves with strong nonlinear features. Despite being a promising method, the recently developed physics-informed neural networks (PINNs) are still not fully effective for calculating discontinuities compared with those of traditional shock-capturing methods.
Li Liu +7 more
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Referenceless Characterisation of Complex Media Using Physics-Informed Neural Networks
, 2023 We accurately characterize the transmission matrices of complex scattering media using a physics-informed, multi-plane neural network (MPNN) without a known reference field.
Malik, Mehul; id_orcid +3 more
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RBF-MGN:Solving spatiotemporal PDEs with Physics-informed Graph Neural Network [PDF]
, 2022 Physics-informed neural networks (PINNs) have lately received significant attention as a representative deep learning-based technique for solving partial differential equations (PDEs).
Xiang, Zixue, Peng, Wei, Yao, Wen
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