  An algorithm to obtain data-driven models of oscillatory phenomena in plasma
space propulsion systems is presented, based on sparse regression (SINDy) and
Pareto front analysis. The algorithm can incorporate physical constraints, use
data bootstrapping for additional robustness, and fine-tuning to different
metrics. Standard, weak and integral SINDy formulations are discussed and
compared. The scheme is benchmarked in the case of breathing-mode oscillations
in Hall effect thrusters, using PIC/fluid simulation data. Models of varying
complexity are obtained for the average plasma properties, and shown to have a
clear physical interpretability and agreement with existing 0D models in the
literature. Lastly, the algorithm applied is also shown to enable the
identification of physical subdomains with qualitatively different plasma
dynamics, providing valuable information for more advanced modeling approaches.
