Improving ideal MHD equilibrium accuracy with physics-informed neural networks

1. 1Parametrize Fourier modes with artificial neural networks
2. 2Minimize the full nonlinear global force residual across the volume in real space using first-order optimizers
3. 3Compare the results with conventional solvers to evaluate accuracy and computational cost

Researchers and engineers working on magnetohydrodynamics and plasma physics can benefit from this approach to improve computational efficiency and accuracy, and it can be applied by ml-researchers and software-engineers in collaboration

💡 Physics-informed neural networks can improve computational efficiency and accuracy in computing three-dimensional Magnetohydrodynamic equilibria

💡 Physics-informed neural networks boost MHD equilibrium accuracy