Results 11 to 20 of about 13,405 (259)
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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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
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Predicting Voltammetry Using Physics-Informed Neural Networks
The Journal of Physical Chemistry Letters, 2022 We propose a discretization-free approach to simulation of cyclic voltammetry using Physics-Informed Neural Networks (PINNs) by constraining a feed-forward neutral network with the diffusion equation and electrochemically consistent boundary conditions.
Chen, H, Kätelhön, E, Compton, RG
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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.
Stefano Markidis
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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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Multifidelity modeling for Physics-Informed Neural Networks (PINNs) [PDF]
Journal of Computational Physics, 2022 Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way. Candidates for this approach are simulation methodologies for which there are fidelity differences connected with significant computational cost differences.
Michael Penwarden +3 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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Conditional physics informed neural networks
Communications in Nonlinear Science and Numerical Simulation, 2022 18 pages, 11 ...
Alexander Kovacs +13 more
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