Results 21 to 30 of about 13,405 (259)
Physics-informed neural networks for solving nonlinear diffusivity and Biot's equations.
PLoS ONE, 2020 This paper presents the potential of applying physics-informed neural networks for solving nonlinear multiphysics problems, which are essential to many fields such as biomedical engineering, earthquake prediction, and underground energy harvesting ...
Teeratorn Kadeethum +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.
Limmer, Steffen +2 more
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Frontiers in Materials, 2022 (Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of
Arnd Koeppe +6 more
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Physics-informed neural networks for modeling astrophysical shocks
Machine Learning: Science and Technology, 2023 Physics-informed neural networks (PINNs) are machine learning models that integrate data-based learning with partial differential equations (PDEs).
S P Moschou +5 more
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Separable Physics-Informed Neural Networks
, 2023 Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental limitation of training PINNs to solve multi-dimensional PDEs and approximate highly complex solution functions.
Cho, Junwoo +5 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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Sobolev training for physics-informed neural networks
Communications in Mathematical Sciences, 2023 Physics Informed Neural Networks (PINNs) is a promising application of deep learning. The smooth architecture of a fully connected neural network is appropriate for finding the solutions of PDEs; the corresponding loss function can also be intuitively designed and guarantees the convergence for various kinds of PDEs.
Son, Hwijae +3 more
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Physics-Informed Neural Networks for Cardiac Activation Mapping
Frontiers in Physics, 2020 A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior ...
Francisco Sahli Costabal +8 more
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Recent Developments in Artificial Intelligence in Oceanography
Ocean-Land-Atmosphere Research, 2022 With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications.
Changming Dong +5 more
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A Taxonomic Survey of Physics-Informed Machine Learning
Applied Sciences, 2023 Physics-informed machine learning (PIML) refers to the emerging area of extracting physically relevant solutions to complex multiscale modeling problems lacking sufficient quantity and veracity of data with learning models informed by physically relevant
Joseph Pateras +2 more
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