He, Ao Shi, Jianping Chen, Jiajun Fang, Hui
Published in
Physica Scripta
The Physics-Informed Neural Network (PINN) has achieved remarkable results in solving partial differential equations (PDEs). This paper aims to solve the forward and inverse problems of some specific nonlinear diffusion convection-reaction equations, thereby validating the practical efficacy and accuracy of data-driven approaches in tackling such e...
NGUYEN, Duc-Vinh JEBAHI, Mohamed CHINESTA SORIA, Francisco
Recent advances have prominently highlighted physics informed neural networks (PINNs) as an efficient methodology for solving partial differential equations (PDEs). The present paper proposes a proof of concept exploring the use of PINNs as an alternative to finite element (FE) solvers in both classical and gradient-enhanced solid mechanics. To thi...
Nguyen, Duc-Vinh Jebahi, Mohamed Chinesta, Francisco
International audience
Khademi, Amirhossein Dufour, Steven
Published in
Physica Scripta
The advancement of scientific machine learning (ML) techniques has led to the development of methods for approximating solutions to nonlinear partial differential equations (PDE) with increased efficiency and accuracy. Automatic differentiation has played a pivotal role in this progress, enabling the creation of physics-informed neural networks (PI...
Schøler, J. P. Rosi, N. Quick, J. Riva, R. Andersen, S. J. Murcia Leon, J. P. Van Der Laan, M. P. Réthoré, P.-E.
Published in
Journal of Physics: Conference Series
Artificial Neural Networks (ANNs) are being applied as a faster alternative to Computational Fluid Dynamics (CFD) for wind turbine engineering wake models. Unfortunately, ANNs can fail to generalize if the data is insufficient. Physics-Informed Neural Networks (PINNs) can improve convergence while lowering the required data amounts. This paper inve...
Auddy, Sayantan Dey, Ramit Turner, Neal J Basu, Shantanu
Published in
Machine Learning: Science and Technology
Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds, galaxy formation, and the development of large-scale structures in the Universe. However, the nonlinear interaction between gravity and fluid dynamics offers a formidab...
Chen, Jingjian Yuan, Chunxin Xu, Jiali Bie, Pengfei Wei, Zhiqiang
Published in
Frontiers in Marine Science
Modified Benney-Luke equation (mBL equation) is a three-dimensional temporal-spatial equation with complex structures, that is a high-dimensional partial differential equation (PDE), it is also a new equation of the physical ocean field, and its solution is important for studying the internal wave-wave interaction of inclined seafloor. For conventi...
Kang, Hanseul
This thesis evaluates the efficacy of Physics-Informed Neural Networks (PINNs) in simulating fluid dynamics challenges, focusing on the Burgers' equation and the lid-driven cavity problem, to develop a robust PINN framework for nuclear engineering applications such as the Sustainable Nuclear Energy Research In Sweden (SUNRISE) project. The research...
Berrone, S Canuto, C Pintore, M Sukumar, N
In this paper, we present and compare four methods to enforce Dirichlet boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (VPINNs). Such conditions are usually imposed by adding penalization terms in the loss function and properly choosing the corresponding scaling coefficients; however...
Alzubaidi, Laith Bai, Jinshuai Al-Sabaawi, Aiman Santamaria, Jose Albahri, A. S. Al-dabbagh, Bashar Sami Nayyef Al-dabbag... Fadhel, Mohammed A. Manoufali, Mohamed Zhang, Jinglan Al-Timemy, Ali H.
...
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast bac...
