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
Actor-Critic Methods using Physics-Informed Neural Networks: Control of a 1D PDE Model for Fluid-Cooled Battery Packs
Amartya Mukherjee, Jun Liu
A Framework Based on Symbolic Regression Coupled with eXtended Physics-Informed Neural Networks for Gray-Box Learning of Equations of Motion from Data
Elham Kiyani, Khemraj Shukla, George Em Karniadakis, Mikko Karttunen
A Survey on Solving and Discovering Differential Equations Using Deep Neural Networks
Hyeonjung, Jung, Jayant Gupta, Bharat Jayaprakash, Matthew Eagon, Harish Panneer Selvam, Carl Molnar, William Northrop, Shashi Shekhar
Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines
Kamaljyoti Nath, Xuhui Meng, Daniel J Smith, George Em Karniadakis
Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models
Simin Shekarpaz, Fanhai Zeng, George Karniadakis
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
Alexandr Sedykh, Maninadh Podapaka, Asel Sagingalieva, Karan Pinto, Markus Pflitsch, Alexey Melnikov
Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations
Shuang Hu, Meiqin Liu, Senlin Zhang, Shanling Dong, Ronghao Zheng