Results 21 to 30 of about 16,533 (261)

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, Lu Lu 0010, George Em Karniadakis   +2 more
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Further investigation of convolutional neural networks applied in computational electromagnetism under physics‐informed consideration

IET Electric Power Applications, 2022
Convolutional neural networks (CNN) have shown great potentials and have been proven to be an effective tool for some image‐based deep learning tasks in the field of computational electromagnetism (CEM).
Ruohan Gong, Zuqi Tang
doaj   +1 more source

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. One of the advantages of using PINN is to leverage the usage of Machine Learning computational frameworks relying on the combined usage of CPUs and co ...
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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, Shengping Liu, Hui Xie, Fansheng Xiong, Tengchao Yu, Mengjuan Xiao, Lufeng Liu, Heng Yong   +7 more
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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, Henning Wessels, Rolf Mahnken   +2 more
openaire   +3 more sources

Quantum Physics Informed Neural Networks

The 53rd International Conference on Parallel Processing Workshops
In this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations. The comparative expressibility of the purely quantum, hybrid and classical neural networks is discussed ...
Pratibha Raghupati Hegde, Stefano Markidis   +1 more
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Physics informed neural networks for triple deck [PDF]

Aircraft Engineering and Aerospace Technology, 2022
Purpose This paper aims to introduce physics-informed neural networks (PINN) applied to the two-dimensional steady-state laminar Navier–Stokes equations over a flat plate with roughness elements and specified local heating. The method bridges the gap between asymptotics theory and three-dimensional turbulent flow analyses, characterized by high costs ...
Abderrahmane, Belkallouche, Rezoug, Tahar, Dala, Laurent, Tan, Kian   +3 more
openaire   +1 more source

Modeling a Typical Non-Uniform Deformation of Materials Using Physics-Informed Deep Learning: Applications to Forward and Inverse Problems

Applied Sciences, 2023
Numerical methods, such as finite element or finite difference, have been widely used in the past decades for modeling solid mechanics problems by solving partial differential equations (PDEs).
Yawen Deng, Changchang Chen, Qingxin Wang, Xiaohe Li, Zide Fan, Yunzi Li   +5 more
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Physics Informed Neural Network for Option Pricing

CoRR, 2023
We apply a physics-informed deep-learning approach the PINN approach to the Black-Scholes equation for pricing American and European options. We test our approach on both simulated as well as real market data, compare it to analytical/numerical benchmarks.
Ashish Dhiman 0002, Yibei Hu
openaire   +2 more sources

Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation

Entropy, 2022
In this paper, a grid-free deep learning method based on a physics-informed neural network is proposed for solving coupled Stokes–Darcy equations with Bever–Joseph–Saffman interface conditions.
Ruilong Pu, Xinlong Feng
doaj   +1 more source