Scientific machine learning through physics–informed neural networks: Where we are and what's next
Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode
model equations, like Partial Differential Equations (PDE), as a component of the neural …
model equations, like Partial Differential Equations (PDE), as a component of the neural …
Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …
the past decade, at the same time, the capability to develop high-fidelity system predictive …
Learning the solution operator of parametric partial differential equations with physics-informed DeepONets
Partial differential equations (PDEs) play a central role in the mathematical analysis and
modeling of complex dynamic processes across all corners of science and engineering …
modeling of complex dynamic processes across all corners of science and engineering …
When and why PINNs fail to train: A neural tangent kernel perspective
Physics-informed neural networks (PINNs) have lately received great attention thanks to
their flexibility in tackling a wide range of forward and inverse problems involving partial …
their flexibility in tackling a wide range of forward and inverse problems involving partial …
Physics-informed neural operator for learning partial differential equations
In this article, we propose physics-informed neural operators (PINO) that combine training
data and physics constraints to learn the solution operator of a given family of parametric …
data and physics constraints to learn the solution operator of a given family of parametric …
Understanding and mitigating gradient flow pathologies in physics-informed neural networks
The widespread use of neural networks across different scientific domains often involves
constraining them to satisfy certain symmetries, conservation laws, or other domain …
constraining them to satisfy certain symmetries, conservation laws, or other domain …
On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
Physics-informed neural networks (PINNs) are demonstrating remarkable promise in
integrating physical models with gappy and noisy observational data, but they still struggle …
integrating physical models with gappy and noisy observational data, but they still struggle …
Self-adaptive loss balanced physics-informed neural networks
Z **ang, W Peng, X Liu, W Yao - Neurocomputing, 2022 - Elsevier
Physics-informed neural networks (PINNs) have received significant attention as a
representative deep learning-based technique for solving partial differential equations …
representative deep learning-based technique for solving partial differential equations …
Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great
potential in the breakthrough of metal additive manufacturing (AM) process modeling, which …
potential in the breakthrough of metal additive manufacturing (AM) process modeling, which …
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
Recently, the advent of deep learning has spurred interest in the development of physics-
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …
informed neural networks (PINN) for efficiently solving partial differential equations (PDEs) …