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Machine Learning-Based Solutions for Aerospace Engineering
Abstract
The incorporation of machine learning (ML) in aircraft engineering has transformed the design, analysis, and operation of intricate aerospace systems. This study examines the present and developing applications of machine learning techniques in critical domains like aircraft design optimisation, defect detection and diagnostics, flight control systems, and predictive maintenance. Utilising extensive information from simulations, sensors, and real-time operations, machine learning models facilitate more efficient decision-making, improved system reliability, and decreased operational costs. Moreover, progress in deep learning, reinforcement learning, and neural networks is being progressively utilised for applications spanning aerodynamic modelling to autonomous flight control. This study emphasises the difficulties related to data quality, interpretability, and model validation in safety-critical aircraft contexts.
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