Results 21 to 30 of about 8,084,029 (292)
Sensors, 2023 A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods.
Dmitriy Tarkhov +2 more
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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 shell structures
European Journal of Mechanics - A/Solids, 2023 ISSN:1873 ...
Jan-Hendrik Bastek, Dennis M. Kochmann
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Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks [PDF]
Heliyon, 2023 22 pages, 45 ...
Berrone, Stefano +3 more
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Innovative Applications of Physics-Informed Neural Networks
, 2023 Innovative Applications of Physics-Informed Neural ...
Vikash Malhotra
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Mathematics, 2023 Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and thus ensure that their predictions are consistent and stable with the physical laws.
Zhixiang Liu +4 more
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, 2023 Application of Physics-Informed Neural Network (PINN) for Understanding Vortex-Induced Vibration with Tunable ...
Mujahid Khan
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Physics-Informed Neural Networks [PDF]
Neural networks have been used extensively in many fields with impressive results.Most applications use data as the sole learning source. Recently, researchers have proposedto use additional information about the latent data that gives birth to information-informedmachine learning.
Georgios E. Stavroulakis +2 more
+7 more sources
Enhanced physics‐informed neural networks for hyperelasticity
International Journal for Numerical Methods in Engineering, 2022 Abstract Physics‐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.
Diab W. Abueidda +3 more
openaire +3 more sources