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) …
been achieved through the numerical discretization of partial differential equations (PDEs) …
Physics-informed neural networks (P INNs): application categories, trends and impact
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) …
(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 …
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 …
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
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 …
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 …
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 …
science and engineering scenarios when data-collection is difficult, or traditional numerical …
A peridynamic-informed deep learning model for brittle damage prediction
Abstract Physics-informed Neural Network (PINN) has been introduced recently to predict
and understand complex physical phenomena by directly incorporating feedback from …
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 …
processes with spatiotemporal dependence. Although physics-informed neural networks …
Residual-Enhanced Physic-Guided Machine Learning with Hard Constraints for Subsurface Flow in Reservoir Engineering
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 …
can enhance our understanding of the development status of oilfields, thus enabling the …