A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials

A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials

This work explores the use of Physics-Informed Neural Networks (PINNs) and a newly proposed approach, called the STacked Adaptive Residual PINN (STAR-PINN), to solve magnetic diffusion problems in the magneto quasi static regime. The study covers both one- and two-dimensional domains. The key advant...

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Bibliographic Details
Main Authors: Shayan Dodge, Sami Barmada, Alessandro Formisano
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Physics-informed neural network (PINN)
magneto-quasi-static (MQS)
time-domain electromagnetics
nonlinear magnetic materials
stacked neural networks
residual network
Online Access:https://ieeexplore.ieee.org/document/11122441/
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https://ieeexplore.ieee.org/document/11122441/

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