Enhanced Lifetime Prediction and Performance Optimization for Xenon Ion Propulsion Using Physics-Informed Neural Networks and Multi-Fidelity Gaussian Process Regression

**Abstract:** This paper introduces a novel framework for enhancing the lifetime prediction and performance optimization of xenon ion propulsion systems. Leveraging physics-informed neural networks (PINNs) and multi-fidelity Gaussian process regression (GP), we develop a hybrid model capable of accurately forecasting long-term system behavior and identifying optimal operating parameters. The approach mitigates limitations of traditional computationally expensive simulations and data-driven methods, enabling real-time performance adjustment and extended mission lifetimes for spacecraft utilizing ion propulsion. This framework directly addresses the critical needs for precision lifetime estimation and operational efficiency in deep-space missions.