Results 21 to 30 of about 80,482 (270)
Investigating molecular transport in the human brain from MRI with physics-informed neural networks
Scientific Reports, 2022 In recent years, a plethora of methods combining neural networks and partial differential equations have been developed. A widely known example are physics-informed neural networks, which solve problems involving partial differential equations by ...
Bastian Zapf +5 more
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Determining COVID-19 Dynamics Using Physics Informed Neural Networks
Axioms, 2022 The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of COVID-19. To provide the governing system of equations used by the framework, the Susceptible–Infected–Recovered–Death mathematical model is used.
Joseph Malinzi +2 more
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Physics-Informed Neural Networks for shell structures
European Journal of Mechanics - A/Solids, 2023 ISSN:1873 ...
Jan-Hendrik Bastek, Dennis M. Kochmann
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Physics-Informed Neural Networks for Pathloss Prediction
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 This paper introduces a physics-informed machine learning approach for pathloss prediction. This is achieved by including in the training phase simultaneously (i) physical dependencies between spatial loss field and (ii) measured pathloss values in the field.
Steffen Limmer +2 more
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Physics-informed neural networks for spherical indentation problems
Materials & Design, 2023 A scientific deep learning (SciDL) approach was developed by integrating a regression-based spherical indentation method with an artificial neural network (ANN) to extract elastic–plastic properties from indentation load-depth curves.
Karuppasamy Pandian Marimuthu +1 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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General implementation of quantum physics-informed neural networks
Array, 2023 Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics.
Shashank Reddy Vadyala +1 more
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Enhanced physics‐informed neural networks for hyperelasticity
International Journal for Numerical Methods in Engineering, 2022 AbstractPhysics‐informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics‐informed neural network models suffer from several issues and can fail to provide accurate solutions in many scenarios. We discuss a few of these challenges and
Diab W. Abueidda +3 more
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fPINNs: Fractional Physics-Informed Neural Networks [PDF]
SIAM Journal on Scientific Computing, 2019 Physics-informed neural networks (PINNs) are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into the NN using automatic differentiation, while the sum of the mean-squared PDE-residuals and the mean ...
Guofei Pang +2 more
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