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
Tackling the Curse of Dimensionality in Fractional and Tempered Fractional PDEs with Physics-Informed Neural Networks
Zheyuan Hu, Kenji Kawaguchi, Zhongqiang Zhang, George Em Karniadakis
Identification of Physical Properties in Acoustic Tubes Using Physics-Informed Neural Networks
Kazuya Yokota, Masataka Ogura, Masajiro Abe
Error Analysis and Numerical Algorithm for PDE Approximation with Hidden-Layer Concatenated Physics Informed Neural Networks
Yianxia Qian, Yongchao Zhang, Suchuan Dong
VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
Seungchan Ko, Sang Hyeon Park
Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems
Abhiram Anand Thiruthummal, Sergiy Shelyag, Eun-jin Kim
FlamePINN-1D: Physics-informed neural networks to solve forward and inverse problems of 1D laminar flames
Jiahao Wu, Su Zhang, Yuxin Wu, Guihua Zhang, Xin Li, Hai Zhang
ConDiff: A Challenging Dataset for Neural Solvers of Partial Differential Equations
Vladislav Trifonov, Alexander Rudikov, Oleg Iliev, Ivan Oseledets, Ekaterina Muravleva
Adaptive Interface-PINNs (AdaI-PINNs): An Efficient Physics-informed Neural Networks Framework for Interface Problems
Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Antareep Kumar Sarma
Element-wise Multiplication Based Deeper Physics-Informed Neural Networks
Feilong Jiang, Xiaonan Hou, Min Xia
Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
Pengsong Yin, Shuo Ling, Wenjun Ying
Physics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology
Sukirt Thakur, Harsa Mitra, Arezoo M. Ardekani
Solving Differential Equations using Physics-Informed Deep Equilibrium Models
Bruno Machado Pacheco, Eduardo Camponogara
Initialization-enhanced Physics-Informed Neural Network with Domain Decomposition (IDPINN)
Chenhao Si, Ming Yan
Multivariate Physics-Informed Convolutional Autoencoder for Anomaly Detection in Power Distribution Systems with High Penetration of DERs
Mehdi Jabbari Zideh, Sarika Khushalani Solanki
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks
Khemraj Shukla, Juan Diego Toscano, Zhicheng Wang, Zongren Zou, George Em Karniadakis