Speaker
Description
The 2D Orszag-Tang vortex magnetohydrodynamics (MHD) problem is studied through the use of physics-constrained convolutional neural networks (PCNNs). The density and the magnetic field are forecasted, and we also predict magnetic field given the velocity field of the fluid. We examined the incorporation of various physics constraints into the PCNNs: absence of magnetic monopoles, non-negativity of density and use of only relevant variables. Translation equivariance was present from the convolutional architecture. The use of a residual architecture and data augmentation was found to increase performance greatly. The most accurate models were incorporated into the simulation, with reasonably accurate results. For the prediction task, the PCNNs were evaluated against a physics-informed neural network (PINN), which had the ideal MHD induction equation as a soft constraint. The use of PCNNs for MHD has the potential to produce physically consistent real-time simulations to serve as virtual diagnostics in cases where inferences must be made with limited observables.
Funding Agency
Los Alamos National Laboratory - Laboratory Directed Research and Development (LDRD)
| Region represented | North America |
|---|---|
| Paper preparation format | LaTeX |
