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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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Parametric Compressible Flow Predictions using Physics-Informed Neural Networks [PDF]
, 2022 The numerical approximation of solutions to the compressible Euler and Navierstokes equations is a crucial but challenging task with relevance in various fields of science and engineering.
Wassing, Simon +2 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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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
doaj +1 more source
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 in Temporal Graphs [PDF]
, 2023 openLo scopo della tesi è quello di sviluppare nuove tecniche di interpretable physics informed machine learning per trovare modelli epidemiologici, e comparare queste tecniche ad altre già esistenti.
VISENTIN, FILIPPO
core
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
doaj +1 more source
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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Discontinuity Computing Using Physics-Informed Neural Networks
Journal of Scientific Computing, 2022 Simulating discontinuities is a long standing problem especially for shock waves with strong nonlinear feather. Despite being a promising method, the recently developed physics-informed neural network (PINN) is still weak for calculating discontinuities compared with traditional shock-capturing methods.
Li Liu +7 more
openaire +3 more sources