A Prediction Model of HPEMP Effect for Vehicles Based on CNN
- Resource Type
- Conference
- Authors
- Cao, Le; Yuan, Hao-Wei; Li, Cheng-Cheng
- Source
- 2022 International Applied Computational Electromagnetics Society Symposium (ACES-China) Applied Computational Electromagnetics Society Symposium (ACES-China), 2022 International. :1-2 Dec, 2022
- Subject
- Engineering Profession
Fields, Waves and Electromagnetics
Space vehicles
Computational modeling
Neural networks
Memory management
Predictive models
Prediction algorithms
Reflection
Vehicle electromagnetic coupling
High-power electromagnetic pulse
Half-space
Deep learning
Convolutional neural network
- Language
This paper proposes a CNN (Convolutional Neural Network)-based prediction model for the HPEMP (High Power Electromagnetic Pulse) effect of ground vehicles, which is used to predict the composite scattering problem of the lower half space and the vehicle above when ground reflections are considered. This method overcomes the traditional Numerical methods are limited by high computational demands and complex half-space environments. The CNN prediction model in this paper provides a reference for evaluating the HPEMP effect of targets on complex half-space media.