Self-adaptive physics-informed neural networks using a soft attention mechanism
L McClenny, U Braga-Neto - arXiv preprint arXiv:2009.04544, 2020 - arxiv.org
Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …
application of deep neural networks to the numerical solution of nonlinear partial differential …
Self-adaptive physics-informed neural networks
LD McClenny, UM Braga-Neto - Journal of Computational Physics, 2023 - Elsevier
Abstract Physics-Informed Neural Networks (PINNs) have emerged recently as a promising
application of deep neural networks to the numerical solution of nonlinear partial differential …
application of deep neural networks to the numerical solution of nonlinear partial differential …
Stiff-PDEs and physics-informed neural networks
P Sharma, L Evans, M Tindall, P Nithiarasu - Archives of Computational …, 2023 - Springer
In recent years, physics-informed neural networks (PINN) have been used to solve stiff-
PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D …
PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D …
The application of physics-informed machine learning in multiphysics modeling in chemical engineering
Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
Physical laws meet machine intelligence: current developments and future directions
The advent of technology including big data has allowed machine learning technology to
strengthen its place in solving different science and engineering complex problems …
strengthen its place in solving different science and engineering complex problems …
Physics-informed neural networks with adaptive localized artificial viscosity
Abstract Physics-informed Neural Network (PINN) is a promising tool that has been applied
in a variety of physical phenomena described by partial differential equations (PDE) …
in a variety of physical phenomena described by partial differential equations (PDE) …
Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: An application to rubber calendering process
TNK Nguyen, T Dairay, R Meunier… - Engineering Applications of …, 2022 - Elsevier
Abstract Physics-Informed Neural Networks (PINNs) have gained much attention in various
fields of engineering thanks to their capability of incorporating physical laws into the models …
fields of engineering thanks to their capability of incorporating physical laws into the models …
Hyper-parameter tuning of physics-informed neural networks: Application to Helmholtz problems
P Escapil-Inchauspé, GA Ruz - Neurocomputing, 2023 - Elsevier
We consider physics-informed neural networks (PINNs)(Raissiet al., 2019) for forward
physical problems. In order to find optimal PINNs configuration, we introduce a hyper …
physical problems. In order to find optimal PINNs configuration, we introduce a hyper …
PFNN-2: A domain decomposed penalty-free neural network method for solving partial differential equations
A new penalty-free neural network method, PFNN-2, is presented for solving partial
differential equations, which is a subsequent improvement of our previously proposed PFNN …
differential equations, which is a subsequent improvement of our previously proposed PFNN …
IDRLnet: A physics-informed neural network library
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve
both forward and inverse problems modeled by Partial Differential Equations (PDEs). This …
both forward and inverse problems modeled by Partial Differential Equations (PDEs). This …