.png){kind=icon}
返回
Physics informed neural network model for multi-particle interaction forces
.png){kind=icon}
PRE
AI
DOI：10.1016/j.partic.2024.11.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The discrete element method (DEM) model calculates interaction forces between each pair of particles. However, it becomes computational expensive especially when the number of particles is large. In this study, a novel artificial neural network (ANN) model is proposed to replace the model of interaction forces between multiple particles in DEM including contact force and electrostatic force. The ANN model combines the residual network (ResNet) with the physics informed neural network (PINN). The physical loss term is derived from the Newton's third law about internal forces in multi-particle system. The performance of the ANN model is evaluated based on the DEM simulation data of 100, 200, and 300-particle system in a wall-bounded 2D swirling flow. It is found that the computing time is reduced nearly an order of magnitude (7-10 times) compared with the DEM model. In addition, the accuracy of the ANN model achieves the R-2 > 0.93 with only <= 2% particles are not well predicted. (c) 2024 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Keyword:
Artificial neural network
ResNet
PINN
Multiphase
DEM
Particle interaction force
期刊
IF:
4.3
论文数:
2.7K
被引数:
6.1K
机构
引用论文
.png){kind=icon}
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架
OAAI
OAAI分享收藏
Testing the concurrent validity of a naturalistic upper extremity reaching task测试自然主义上肢到达任务的同时有效性OAAI
OAAI分享收藏Cu-Ni Thin Films Electrodeposited on Si: Composition and Current Efficiency电沉积在Si上的cu-ni薄膜: 组成和电流效率
PREAI
分享收藏
查看更多内容