IEEE

Transfer Learning Fourier Neural Operator for Solving Parametric Frequency-Domain Wave Equations

Abstract: Fourier neural operator (FNO) is a recently proposed data-driven scheme to approximate the implicit operators characterized by partial differential equations (PDEs) between functional spaces ...
Microsoft

LordNet: An efficient neural network for learning to solve parametric partial differential equations without simulated data

Neural operators, as a powerful approximation to the non-linear operators between infinite-dimensional function spaces, have proved to be promising in accelerating the solution of partial differential ...
IEEE

Solving Combined Field Integral Equations With Physics-Informed Graph Residual Learning for EM Scattering of 3-D PEC Targets

Abstract: In this study, physics-informed graph residual learning (PhiGRL) is proposed as an effective and robust deep learning (DL)-based approach for 3-D electromagnetic (EM) modeling. Extended from ...
Frontiers

Research on intelligent predicting method of underwater acoustic field based on physics-informed neural network

In the context of the rapid development of computer hardware and the continuous improvement of the artificial intelligence and deep learning theory, aiming at the traditional numerical solution method ...
GitHub

Tutorial codes for a physics informed machine learning lecture based on shallow water equation.

All executable code is in the notebook swe_instructor_code_retreat.ipynb. To directly open the notebook in Google Colab, click the badge below.