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
EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations
Ayoub Farkane, Mounir Ghogho, Mustapha Oudani, Mohamed Boutayeb
A physics-informed neural network framework for modeling obstacle-related equations
Hamid El Bahja, Jan Christian Hauffen, Peter Jung, Bubacarr Bah, Issa Karambal
Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity
Ali Kashefi, Leonidas J. Guibas, Tapan Mukerji
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time
Yanxia Qian, Yongchao Zhang, Yunqing Huang, Suchuan Dong
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment
Rahul Nellikkath, Andreas Venzke, Mohammad Kazem Bakhshizadeh, Ilgiz Murzakhanov, Spyros Chatzivasileiadis
ChatGPT for Programming Numerical Methods
Ali Kashefi, Tapan Mukerji
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations
Wenqian Chen, Panos Stinis