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The integration of artificial intelligence (AI) in the space industry is evolving to address complex engineering challenges. Engineers face hurdles such as data insufficiency and the need for effective verification and validation of AI models. Simulation plays a crucial role in generating synthetic data, which complements real-world data and enhances model reliability.
Three main intersections of AI and simulation are identified: overcoming data challenges, approximating complex systems, and developing algorithms. Tools like Simulink and MATLAB enable engineers to create simulated environments that mirror real-world scenarios, facilitating the design and testing of AI models. As AI continues to advance, its collaboration with simulation will be vital for future space applications.
• AI models depend heavily on the quality of training data.
• Simulation can synthesize data that is difficult or expensive to collect.
Data-centric AI emphasizes the importance of high-quality training data for model accuracy.
Reduced-order modeling allows engineers to approximate complex systems with less computational expense.
Reinforcement learning learns entire control strategies, enhancing applications in robotics and autonomous systems.
MathWorks provides tools like Simulink and MATLAB, essential for simulating and testing AI models in engineering.
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