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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11122441/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849736734515920896 |
|---|---|
| author | Shayan Dodge Sami Barmada Alessandro Formisano |
| author_facet | Shayan Dodge Sami Barmada Alessandro Formisano |
| author_sort | Shayan Dodge |
| collection | DOAJ |
| description | 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 advantage of this new architecture is the ability to refine predictions through multiple lightweight PINN blocks to achieve accurate results with lower computational cost and less architectural complexity than more advanced neural networks like Recurrent Neural Networks or Convolutional Neural Networks. The simplicity and efficiency of STAR-PINN make it a promising solution for tackling large-scale and nonlinear challenges in computational electromagnetics. |
| format | Article |
| id | doaj-art-5450f7727c0a45de9bca46f73fade13c |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5450f7727c0a45de9bca46f73fade13c2025-08-20T03:07:11ZengIEEEIEEE Access2169-35362025-01-011314138014139410.1109/ACCESS.2025.359786911122441A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear MaterialsShayan Dodge0Sami Barmada1https://orcid.org/0000-0003-1414-1114Alessandro Formisano2https://orcid.org/0000-0002-7007-5759DESTeC, University of Pisa, Pisa, ItalyDESTeC, University of Pisa, Pisa, ItalyDipartimento di Ingegneria, Università degli Studi della Campania “Luigi Vanvitelli,”, Aversa, ItalyThis 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 advantage of this new architecture is the ability to refine predictions through multiple lightweight PINN blocks to achieve accurate results with lower computational cost and less architectural complexity than more advanced neural networks like Recurrent Neural Networks or Convolutional Neural Networks. The simplicity and efficiency of STAR-PINN make it a promising solution for tackling large-scale and nonlinear challenges in computational electromagnetics.https://ieeexplore.ieee.org/document/11122441/Physics-informed neural network (PINN)magneto-quasi-static (MQS)time-domain electromagneticsnonlinear magnetic materialsstacked neural networksresidual network |
| spellingShingle | Shayan Dodge Sami Barmada Alessandro Formisano A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials IEEE Access Physics-informed neural network (PINN) magneto-quasi-static (MQS) time-domain electromagnetics nonlinear magnetic materials stacked neural networks residual network |
| title | A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials |
| title_full | A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials |
| title_fullStr | A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials |
| title_full_unstemmed | A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials |
| title_short | A STacked Adaptive Residual PINN (STAR-PINN) Approach to 2D Time-Domain Magnetic Diffusion in Nonlinear Materials |
| title_sort | stacked adaptive residual pinn star pinn approach to 2d time domain magnetic diffusion in nonlinear materials |
| topic | Physics-informed neural network (PINN) magneto-quasi-static (MQS) time-domain electromagnetics nonlinear magnetic materials stacked neural networks residual network |
| url | https://ieeexplore.ieee.org/document/11122441/ |
| work_keys_str_mv | AT shayandodge astackedadaptiveresidualpinnstarpinnapproachto2dtimedomainmagneticdiffusioninnonlinearmaterials AT samibarmada astackedadaptiveresidualpinnstarpinnapproachto2dtimedomainmagneticdiffusioninnonlinearmaterials AT alessandroformisano astackedadaptiveresidualpinnstarpinnapproachto2dtimedomainmagneticdiffusioninnonlinearmaterials AT shayandodge stackedadaptiveresidualpinnstarpinnapproachto2dtimedomainmagneticdiffusioninnonlinearmaterials AT samibarmada stackedadaptiveresidualpinnstarpinnapproachto2dtimedomainmagneticdiffusioninnonlinearmaterials AT alessandroformisano stackedadaptiveresidualpinnstarpinnapproachto2dtimedomainmagneticdiffusioninnonlinearmaterials |