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
Macroscopic auxiliary asymptotic preserving neural networks for the linear radiative transfer equations
Hongyan Li, Song Jiang, Wenjun Sun, Liwei Xu, Guanyu Zhou
PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution
Shulei Ni, Yisheng Qiu, Yunchun Chen, Zihao Song, Hao Chen, Xuejian Jiang, Huaxi Chen
Understanding the training of PINNs for unsteady flow past a plunging foil through the lens of input subdomain level loss function gradients
Rahul Sundar, Didier Lucor, Sunetra Sarkar
Two-scale Neural Networks for Partial Differential Equations with Small Parameters
Qiao Zhuang, Chris Ziyi Yao, Zhongqiang Zhang, George Em Karniadakis
Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
Tobias Würth, Niklas Freymuth, Clemens Zimmerling, Gerhard Neumann, Luise Kärger
Hermite Neural Network Simulation for Solving the 2D Schrodinger Equation
Kourosh Parand, Aida Pakniyat
PINN-BO: A Black-box Optimization Algorithm using Physics-Informed Neural Networks
Dat Phan-Trong, Hung The Tran, Alistair Shilton, Sunil Gupta
Learning solutions of parametric Navier-Stokes with physics-informed neural networks
M. Naderibeni, M. J. T. Reinders, L. Wu, D. M. J. Tax
Architectural Strategies for the optimization of Physics-Informed Neural Networks
Hemanth Saratchandran, Shin-Fang Chng, Simon Lucey