Fast Neural Network based Solving of Partial Differential Equations
arXiv:2205.08978v22 citationsh-index: 22
Originality Incremental advance
AI Analysis
This addresses computational efficiency for researchers and engineers in fields like physics and engineering, but appears incremental as it builds on existing methods.
The paper tackles solving partial differential equations (PDEs) using neural networks, achieving faster convergence compared to classic Physically Informed Neural Network (PINN) approaches.
We present a novel method for using Neural Networks (NNs) for finding solutions to a class of Partial Differential Equations (PDEs). Our method builds on recent advances in Neural Radiance Field research (NeRFs) and allows for a NN to converge to a PDE solution much faster than classic Physically Informed Neural Network (PINNs) approaches.