Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications

H Hu, L Qi, X Chao - Thin-Walled Structures, 2024 - Elsevier
For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …

Physics-informed neural networks (P INNs): application categories, trends and impact

M Ghalambaz, MA Sheremet, MA Khan… - International Journal of …, 2024 - emerald.com
Purpose This study aims to explore the evolving field of physics-informed neural networks
(PINNs) through an analysis of 996 records retrieved from the Web of Science (WoS) …

Accurate prediction of discontinuous crack paths in random porous media via a generative deep learning model

Y He, Y Tan, M Yang, Y Wang, Y Xu, J Yuan… - Proceedings of the …, 2024 - pnas.org
Pore structures provide extra freedoms for the design of porous media, leading to desirable
properties, such as high catalytic rate, energy storage efficiency, and specific strength. This …

Physics-informed neural network frameworks for crack simulation based on minimized peridynamic potential energy

L Ning, Z Cai, H Dong, Y Liu, W Wang - Computer Methods in Applied …, 2023 - Elsevier
Physics-informed neural networks (PINNs), which are promising tools for solving nonlinear
equations in the absence of labeled data, have been successfully applied for continuum …

A Space-Time Partial Differential Equation Based Physics-Guided Neural Network for Sea Surface Temperature Prediction

T Yuan, J Zhu, W Wang, J Lu, X Wang, X Li, K Ren - Remote Sensing, 2023 - mdpi.com
Sea surface temperature (SST) prediction has attracted increasing attention, due to its
crucial role in understanding the Earth's climate and ocean system. Existing SST prediction …

Transfer learning-based coupling of smoothed finite element method and physics-informed neural network for solving elastoplastic inverse problems

M Zhou, G Mei - Mathematics, 2023 - mdpi.com
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 …

A reality-augmented adaptive physics informed machine learning method for efficient heat transfer prediction in laser melting

Q Zhu, Z Lu, Y Hu - Journal of Manufacturing Processes, 2024 - Elsevier
Physics informed neural network (PINN) method is proposed to alleviate problems in many
science and engineering scenarios when data-collection is difficult, or traditional numerical …

A peridynamic-informed deep learning model for brittle damage prediction

R Eghbalpoor, A Sheidaei - Theoretical and Applied Fracture Mechanics, 2024 - Elsevier
Abstract Physics-informed Neural Network (PINN) has been introduced recently to predict
and understand complex physical phenomena by directly incorporating feedback from …

Solving PDEs with unmeasurable source terms using coupled physics-informed neural network with recurrent prediction for soft sensors

A Wang, P Qin, XM Sun - arxiv preprint arxiv:2301.08618, 2023 - arxiv.org
Partial differential equations (PDEs) are a model candidate for soft sensors in industrial
processes with spatiotemporal dependence. Although physics-informed neural networks …

Residual-Enhanced Physic-Guided Machine Learning with Hard Constraints for Subsurface Flow in Reservoir Engineering

H Cheng, Y He, P Zeng… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Subsurface flow is the core of reservoir engineering. Research on subsurface flow problems
can enhance our understanding of the development status of oilfields, thus enabling the …