Self-Adaptive Physics-Informed Neural Networks for Solving 2-D Magnetostatic Fields in Open Boundaries
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Magnetics, 2025, PP, (99), pp. 1-1
- Issue Date:
- 2025-01-01
Open Access
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is open access.
Open-boundary 2-D magnetostatic field problems are computationally challenging for traditional numerical methods. This work proposes MagOpenNet, a physics-informed neural network (PINN) framework that introduces the concept of the 1-D and 2-D Astley Infinite Element Method (IEM), along with a self-adaptive weighting strategy, to address the complexities of predictions for both interior and exterior domains. Benchmark tests on circular geometry show that MagOpenNet attains at least 73.2 % reduction compared to the Finite Element Method (FEM) in computation time, 15.58 % reduction compared to IEM and 73.42 % reduction compared to the Finite Difference Method (FDM), with an average of 98.6 % accuracy. The transfer learning-based bus duct, C-core, and U-core benchmarks yield a similar outcome. Compared to traditional PINNs, MagOpenNet achieves up to 36.61 % time reduction in training. These results demonstrate that MagOpenNet provides an alternative for high computational efficiency, high accuracy, strong generalisation ability and stable convergence for open boundary magnetostatic analyses.
Please use this identifier to cite or link to this item: