Hybrid Fourier Neural Operator-Plasma Fluid Model for Fast and Accurate Multiscale Simulations of High Power Microwave Breakdown

2025-09-06

AI Summary

High-power microwave (HPM) breakdown constitutes a prototypical multiscale, strongly coupled problem; conventional full-physics simulations—requiring concurrent solution of Maxwell’s equations and plasma fluid equations—entail prohibitive computational cost. This paper proposes a deep learning–enhanced hybrid modeling paradigm: a Fourier neural operator (FNO) replaces the computationally expensive finite-difference time-domain (FDTD) electromagnetic solver, while remaining tightly coupled with a physics-based plasma fluid model to enable rapid electromagnetic field updates and high-fidelity plasma dynamics simulation. The framework enables seamless integration of legacy C-language simulation code with the Python machine learning ecosystem and is trained and validated on in-house FDTD-generated data. Experiments demonstrate that the model accurately predicts ionization filament morphology, propagation velocity, and spatiotemporal evolution under unseen electric field conditions, achieving a 60× speedup in inference over traditional methods—thereby significantly alleviating the real-time multiscale multiphysics simulation bottleneck.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Modeling and simulation of High Power Microwave (HPM) breakdown, a multiscale phenomenon, is computationally expensive and requires solving Maxwell's equations (EM solver) coupled with a plasma continuity equation (plasma solver). In this work, we present a hybrid modeling approach that combines the accuracy of a differential equation-based plasma fluid solver with the computational efficiency of FNO (Fourier Neural Operator) based EM solver. Trained on data from an in-house FDTD-based plasma-fluid solver, the FNO replaces computationally expensive EM field updates, while the plasma solver governs the dynamic plasma response. The hybrid model is validated on microwave streamer formation, due to diffusion ionization mechanism, in a 2D scenario for unseen incident electric fields corresponding to entirely new plasma streamer simulations not included in model training, showing excellent agreement with FDTD based fluid simulations in terms of streamer shape, velocity, and temporal evolution. This hybrid FNO based strategy delivers significant acceleration of the order of 60X compared to traditional simulations for the specified problem size and offers an efficient alternative for computationally demanding multiscale and multiphysics simulations involved in HPM breakdown. Our work also demonstrate how such hybrid pipelines can be used to seamlessly to integrate existing C-based simulation codes with Python-based machine learning frameworks for simulations of plasma science and engineering problems.

Problem

• Accelerating multiscale HPM breakdown simulations
• Replacing expensive EM solver with efficient FNO
• Integrating differential equations with machine learning

Innovation

• Hybrid Fourier Neural Operator with plasma fluid model
• FNO replaces expensive EM field updates
• Achieves 60X acceleration for multiscale simulations