Data-Efficient Hybrid Physics-Informed Machine Learning for Aircraft Attitude Prediction Under Unseen Turbulence Conditions.

Data-Efficient Hybrid Physics-Informed Machine Learning for Aircraft Attitude Prediction Under Unseen Turbulence Conditions This research proposes a hybrid physics-informed machine learning (H-PIML) framework for aircraft attitude prediction under uncertain and previously unseen atmospheric turbulence conditions. The methodology integrates data-driven neural network models with aircraft rigid-body dynamics by incorporating physics-based constraints into the learning objective. The primary objective is to investigate whether this integration can enhance predictive accuracy, physical consistency, and generalization while reducing dependence on extensive training data. Using computationally simulated flight data and established turbulence models, the framework can be evaluated against conventional machine learning approaches in terms of prediction error, robustness under distribution shifts, data efficiency, and computational performance. This study aims to contribute to the development of reliable, data-efficient predictive models for aerospace applications, with potential relevance to flight simulation, aircraft state estimation, and future real-time flight-control research. The research has potential relevance to the modern aviation industry, where turbulence remains a significant operational challenge for commercial and other aircraft. More reliable aircraft attitude predictions could support turbulence-response analysis, flight simulation, aircraft state-estimation systems, and the development and testing of advanced flight-control algorithms. By investigating model performance under unfamiliar gust conditions and with limited training data, this approach could help researchers assess the feasibility of more adaptable predictive systems for diverse flight environments. In the longer term, the framework may contribute to improved turbulence-management strategies, enhanced pilot-support technologies, and more robust autonomous flight research. However, practical deployment in operational aircraft would require extensive validation using high-fidelity simulations, real flight data, and appropriate aviation safety and certification processes.