Fast electromagnetic field simulation using a current-density- based physics-informed neural network
Zhiwei Gao, Cheng-An Sun, Zibin Ma, Zhijie Liu, Li Zhao, Qingmin Wang · 2025
Read Fast electromagnetic field simulation using a current-density- based physics-informed neural network by Zhiwei Gao, Cheng-An Sun, Zibin Ma, Zhijie Liu, Li Zhao, Qingmin Wang (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12830955.txt.
Description
Zhiwei Gao and five co-authors speed up electric and magnetic field simulation with a neural network, in this twenty twenty-five study. Classic solvers are slow and rigid when fields must be computed again and again. A physics-informed network learns the rules of the field itself, then solves fast.
Their network is built around current density, the flow of charge that drives the field. It was tested on two hard tasks: pulses from laser strikes on targets, and fields in linked circuit problems. The learned solver matched the physics while running with more ease and flex. Such tools could aid design in power and defense tech.
Catalog record
| CREATOR | Zhiwei Gao, Cheng-An Sun, Zibin Ma, Zhijie Liu, Li Zhao, Qingmin Wang |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 44 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |