AI in Wind Turbines
The discussion highlights the importance of identifying defects in wind turbine blades and how design margins can mitigate risks. Insights into training AI models reveal challenges like overfitting due to limited datasets, leading to innovative solutions that combine lab and synthetic data with factory inputs. The approach emphasizes a decentralized system for real-time decision-making while maintaining a centralized model update strategy across global manufacturing sites.In this clip
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The AI Podcast
GE's Danielle Merfeld and Arvind Rangarajan on AI and Renewable Energy - Ep. 150
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