Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications

Tu, Jingzhi; Liu, Chun; Qi, Pian

doi:10.1109/tii.2022.3201985

Cited by 16 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Originally, research has focused on surrogate modeling with PINNs for solving systems governed by the Burgers' and Navier-Stokes equations [26]. PINNs have recently been investigated in industry informatics settings such as modeling flow equations for ocean models [24], modeling crack propagation [27][28], modeling leakage [29], modeling faults [30], and modeling electric loads [31]. Forecasting SST is commonly found as a full-coverage modeling problem combining either generative models [32] [33] or convolutional neural networks [34] with various PDEs.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Buoy Observations Using Physics-Informed Neural Networks

Schmidt,

Pokhrel,

Abdelguerfi

et al. 2024

Preprint

View full text Add to dashboard Cite

Methodologies inspired by physics-informed neural networks (PINNs) were used to forecast observations recorded by stationary ocean buoys. We combined buoy observations with numerical models to train surrogate deep learning networks that performed better than with either data alone. Numerical model outputs were collected from two sources for training and regularization: the hybrid circulation ocean model and the fifth ECMWF reanalysis experiment. A hyperparameter determines the ratio of observational and modeled data to be used in the training procedure, so we conducted a grid search to find the most performant ratio. Overall, the technique improved the general forecast performance compared with nonregularized models. Under specific circumstances, the regularization mechanism enabled the PINN models to be more accurate than the numerical models. This demonstrates the utility of combining various climate models and sensor observations to improve surrogate modeling.

show abstract

Section: Related Workmentioning

confidence: 99%

Forecasting Buoy Observations Using Physics-Informed Neural Networks

Schmidt,

Pokhrel,

Abdelguerfi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…PINNs leverage prior knowledge by integrating observational data and mathematical models to efficiently solve both forward and inverse problems of PDEs. Currently, PINNs have demonstrated remarkable success in various mechanics fields, including material mechanics [38], fluid mechanics [49], fracture mechanics [50], and thermodynamics [51]. Several PINN variants have emerged to address different problems, including conservation PINNs (cPINNs) [52], variational PINNs (vPINNs) [53], fractional-order PINNs (fPINNs) [54], and others.…”

Section: Physics-informed Neural Network (Pinn)mentioning

confidence: 99%

Transfer Learning-Based Coupling of Smoothed Finite Element Method and Physics-Informed Neural Network for Solving Elastoplastic Inverse Problems

Zhou

Mei

2023

Mathematics

View full text Add to dashboard Cite

In practical engineering applications, there is a high demand for inverting parameters for various materials, and obtaining monitoring data can be costly. Traditional inverse methods often involve tedious computational processes, require significant computational effort, and exhibit slow convergence speeds. The recently proposed Physics-Informed Neural Network (PINN) has shown great potential in solving inverse problems. Therefore, in this paper, we propose a transfer learning-based coupling of the Smoothed Finite Element Method (S-FEM) and PINN methods for the inversion of parameters in elastic-plasticity problems. The aim is to improve the accuracy and efficiency of parameter inversion for different elastic-plastic materials with limited data. High-quality small datasets were synthesized using S-FEM and subsequently combined with PINN for pre-training purposes. The parameters of the pre-trained model were saved and used as the initial state for the PINN model in the inversion of new material parameters. The inversion performance of the coupling of S-FEM and PINN is compared with the coupling of the conventional Finite Element Method (FEM) and PINN on a small data set. Additionally, we compared the efficiency and accuracy of both the transfer learning-based and non-transfer learning-based methods of the coupling of S-FEM and PINN in the inversion of different material parameters. The results show that: (1) our method performs well on small datasets, with an inversion error of essentially less than 2%; (2) our approach outperforms the coupling of conventional FEM and PINN in terms of both computational accuracy and computational efficiency; and (3) our approach is at least twice as efficient as the coupling of S-FEM and PINN without transfer learning, while still maintaining accuracy. Our method is well-suited for the inversion of different material parameters using only small datasets. The use of transfer learning greatly improves computational efficiency, making our method an efficient and accurate solution for reducing computational cost and complexity in practical engineering applications.

show abstract

“…The rise of deep learning has ushered in a new era for point cloud segmentation. Neural network architectures, initially designed for 2D image data, have been adapted to cater to the unique structure of point cloud data, showing promising results [ 28 ]. These deep learning-based methods, leveraging vast amounts of data and computational power, have set new benchmarks in point cloud segmentation tasks, often outperforming traditional methods in terms of accuracy and robustness [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

Three-Dimensional Point Cloud Segmentation Algorithm Based on Depth Camera for Large Size Model Point Cloud Unsupervised Class Segmentation

Fang,

Xu,

et al. 2023

Sensors

View full text Add to dashboard Cite

This paper proposes a 3D point cloud segmentation algorithm based on a depth camera for large-scale model point cloud unsupervised class segmentation. The algorithm utilizes depth information obtained from a depth camera and a voxelization technique to reduce the size of the point cloud, and then uses clustering methods to segment the voxels based on their density and distance to the camera. Experimental results show that the proposed algorithm achieves high segmentation accuracy and fast segmentation speed on various large-scale model point clouds. Compared with recent similar works, the algorithm demonstrates superior performance in terms of accuracy metrics, with an average Intersection over Union (IoU) of 90.2% on our own benchmark dataset.

show abstract

Physics-Informed Neural Network Integrating PointNet-Based Adaptive Refinement for Investigating Crack Propagation in Industrial Applications

Cited by 16 publications

References 27 publications

Forecasting Buoy Observations Using Physics-Informed Neural Networks

Forecasting Buoy Observations Using Physics-Informed Neural Networks

Transfer Learning-Based Coupling of Smoothed Finite Element Method and Physics-Informed Neural Network for Solving Elastoplastic Inverse Problems

Three-Dimensional Point Cloud Segmentation Algorithm Based on Depth Camera for Large Size Model Point Cloud Unsupervised Class Segmentation

Contact Info

Product

Resources

About