About Code for reproducing the paper: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks

May 2, 2022 · To benefit from both methods, we propose the Residual-based Adaptive Node Generation (RANG) approach for efficient training of PINNs, which is based on a variable density nodal distribution method for RBF-FD. The method is also enhanced by a memory mechanism to further improve training stability.

May 2, 2022 · To benefit from both methods, we propose the Residual-based Adaptive Node Generation (RANG) approach for efficient training of PINNs, which is based on a variable density nodal...

The Residual-based Adaptive Node Generation method (RANG) is then proposed by combining the residual of PINN with the method that can dynamically change the sampling radius in the computational domain.

Code for reproducing the paper: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks - weipengOO98/rang_pinn

To benefit from both methods, we propose the Residual-based Adaptive Node Generation (RANG) approach for efficient training of PINNs, which is based on a variable density nodal distribution method for RBF-FD. The method is also enhanced by a memory mechanism to further improve training stability.

Feb 1, 2023 · Self-Adaptive PINNs is a new paradigm for training PINNs using trainable weights. The SA training regime focuses on where error is the highest in the domain. We demonstrate the use of SGD to mitigate full-batch computational limitations. We show the SA-PINN improves gradient dynamics during training via NTK analysis.

Jan 1, 2023 · To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training.

The Residual-based Adaptive Node Generation (RANG) approach for efficient training of PINNs is proposed, which is based on a variable density nodal distribution method for RBF-FD and enhanced by a memory mechanism to further improve training stability.

Forked from weipengOO98/rang_pinn Code for reproducing the paper: RANG: A Residual-based Adaptive Node Generation Method for Physics-Informed Neural Networks Python 2