Efficient and Accurate Model Order Reduction for Integral Electromagnetic Formulations in Fusion Device Transient Analysis Toward AI-Enabled Modeling
The numerical simulation of electromagnetic transients in fusion devices is essential for analyzing plasma stability and disruptive events. However, it remains computationally demanding due to the large-scale dense systems arising from integral formulations. This work proposes a model order reduction (MOR) strategy for transient electromagnetic problems based on integral formulations. Unlike operator-based compression techniques (such as $\mathcal{H}$-matrix approaches), the reduced space is constructed directly from the transient excitation. In contrast to classical snapshot- and transfer-function-based MOR approaches, the proposed formulation avoids repeated explicit inversions or factorizations of the dense integral operator during the MOR basis-construction stage. By combining wavelet-based temporal compression with source-driven Krylov projections, the method generates reduced models tailored to the dynamically reachable responses of the prescribed excitation families. Numerical validations on various plasma events and fusion-relevant scenarios demonstrate the robustness of the strategy, achieving substantial computational speedups while accurately preserving the transient electromagnetic response. Finally, the method is successfully applied to the null-field problem to efficiently generate training data for neural-network surrogates, contributing toward physics-consistent AI-enabled fusion modelling.