Physics-Informed Neural Networks for Inverse Electromagnetic Problems

Physics-informed neural networks (PINNs) have been successfully applied in electromagnetism for the solution of direct problems. However, since PINNs typically do not take system parameters (like geometry or material properties) as input, when embedded in inverse problems or adopted for parametrical...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2023-05, Vol.59 (5), p.1-1
Main Authors: Baldan, Marco, Di Barba, Paolo, Lowther, David A.
Format: Article
Language:English
Subjects:
Coil Design
Electromagnetism
Finite element analysis
Inverse Problem
Inverse problems
Magnetic domains
Magnetic Field
Magnetism
Magnetostatics
Material properties
Neural networks
Parameters
Physics-Informed Neural Network
Real-time systems
Training
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Summary:Physics-informed neural networks (PINNs) have been successfully applied in electromagnetism for the solution of direct problems. However, since PINNs typically do not take system parameters (like geometry or material properties) as input, when embedded in inverse problems or adopted for parametrical studies, in order to output the solution of the governing equations, they require additional training for each new system parameter set. To overcome this issue, we propose a hypernetwork (HNN) that receives system parameters and outputs the network weights of a PINN, which in turn provides the solution of the direct problem. Therefore, once trained, the HNN acts as a parametrized real-time field solver that allows the fast solution of inverse problems, in which the objective(s) are defined a-posteriori (i.e., after HNN's training). This method is adopted for a coil optimal design task in magnetostatics.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2023.3247023