Physic Informed Neural Network
Physics-informed neural networks (PINNs) integrate physical laws, typically expressed as differential equations, into neural network training to solve complex scientific problems. Current research focuses on improving PINN accuracy and efficiency through architectural innovations like Fourier-based networks, Kolmogorov-Arnold networks, and wavelet-based approaches, as well as advanced optimization strategies such as dual cone gradient descent and DiffGrad. These advancements aim to overcome limitations in handling high-frequency solutions, complex geometries, and stiff equations, ultimately enhancing the applicability of PINNs across diverse scientific and engineering domains, including fluid dynamics, seismology, and materials science.
Papers
Physics-Informed Neural Networks and Extensions
Maziar Raissi, Paris Perdikaris, Nazanin Ahmadi, George Em Karniadakis
sEMG-Driven Physics-Informed Gated Recurrent Networks for Modeling Upper Limb Multi-Joint Movement Dynamics
Rajnish Kumar, Anand Gupta, Suriya Prakash Muthukrishnan, Lalan Kumar, Sitikantha Roy
Spectral Informed Neural Network: An Efficient and Low-Memory PINN
Tianchi Yu, Yiming Qi, Ivan Oseledets, Shiyi Chen
Stability Analysis of Physics-Informed Neural Networks for Stiff Linear Differential Equations
Gianluca Fabiani, Erik Bollt, Constantinos Siettos, Athanasios N. Yannacopoulos
Domain-decoupled Physics-informed Neural Networks with Closed-form Gradients for Fast Model Learning of Dynamical Systems
Henrik Krauss, Tim-Lukas Habich, Max Bartholdt, Thomas Seel, Moritz Schappler
GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks
Nisal Ranasinghe, Yu Xia, Sachith Seneviratne, Saman Halgamuge
General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
Sen Wang, Peizhi Zhao, Qinglong Ma, Tao Song