Results 1 to 10 of about 1,138 (118)

A Physics-Informed Neural Network (PINN) Approach to Over-Equilibrium Dynamics in Conservatively Perturbed Linear Equilibrium Systems [PDF]

Entropy
Conservatively perturbed equilibrium (CPE) experiments yield transient concentration extrema that surpass steady-state equilibrium values. A physics-informed neural network (PINN) framework is introduced to simulate these over-equilibrium dynamics in ...
Abhishek Dutta, Bitan Mukherjee, Sk Aftab Hosen, Meltem Turan, Denis Constales, Gregory Yablonsky   +5 more
doaj   +2 more sources

MHA-PINN: A Novel Physics-Informed Neural Network for Predicting Fiber Dyeability [PDF]

Sensors
Fiber dyeability is a core indicator of textile quality and added value. Pre-experiment accurate prediction of fiber dyeability reduces the waste and inefficiency of trial-and-error methods.
Feier Zhou, Yuxiang Liu, Shuo Yang, Fan Guo, Xiaofeng Yuan, Ruimin Xie   +5 more
doaj   +2 more sources

Combination of Physics-Informed Neural Networks and Single-Relaxation-Time Lattice Boltzmann Method for Solving Inverse Problems in Fluid Mechanics

Mathematics, 2023
Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and thus ensure that their predictions are consistent and stable with the physical laws.
Zhixiang Liu, Yuanji Chen, Ge Song, Wei Song, Jingxiang Xu   +4 more
doaj   +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
doaj   +1 more source

A Physics-Informed Machine Learning Approach for Estimating Lithium-Ion Battery Temperature

IEEE Access, 2022
The physics-informed neural network (PINN) has drawn much attention as it can reduce training data size and eliminate the need for physics equation identification.
Gyouho Cho, Mengqi Wang, Youngki Kim, Jaerock Kwon, Wencong Su   +4 more
doaj   +1 more source

Physics-informed deep learning for incompressible laminar flows

Theoretical and Applied Mechanics Letters, 2020
: Physics-informed deep learning has drawn tremendous interest in recent years to solve computational physics problems, whose basic concept is to embed physical laws to constrain/inform neural networks, with the need of less data for training a reliable ...
Chengping Rao, Hao Sun, Yang Liu
doaj   +1 more source

Data-driven nondegenerate bound-state solitons of multicomponent Bose–Einstein condensates via mix-training PINN

Results in Physics, 2023
In this paper, by modifying loss function MSE and training area of the physics-informed neural network (PINN), we proposed two neural network models: mix-training PINN and prior information mix-training PINN.
Shifang Tian, Chenchen Cao, Biao Li
doaj   +1 more source

Constructing Physics-Informed Neural Networks with Architecture Based on Analytical Modification of Numerical Methods by Solving the Problem of Modelling Processes in a Chemical Reactor

Sensors, 2023
A novel type of neural network with an architecture based on physics is proposed. The network structure builds on a body of analytical modifications of classical numerical methods.
Dmitriy Tarkhov, Tatiana Lazovskaya, Galina Malykhina   +2 more
doaj   +1 more source

Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network

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, Chao Guo, Mingrui Liu, Rui Shi   +3 more
doaj   +1 more source

Application of physics-informed neural network in the analysis of hydrodynamic lubrication

Friction, 2022
The last decade has witnessed a surge of interest in artificial neural network in many different areas of scientific research. Despite the rapid expansion in the application of neural networks, few efforts have been carried out to introduce such a ...
Yang Zhao, Liang Guo, Patrick Pat Lam Wong   +2 more
doaj   +1 more source