{"id":8440,"date":"2024-08-05T14:42:59","date_gmt":"2024-08-05T13:42:59","guid":{"rendered":"https:\/\/edrmedeso1.wpenginepowered.com\/?post_type=article&p=8440"},"modified":"2024-09-12T15:00:03","modified_gmt":"2024-09-12T14:00:03","slug":"harnessing-the-power-of-ai-to-revolutionize-engineering-simulation","status":"publish","type":"article","link":"https:\/\/edrmedeso.com\/article\/harnessing-the-power-of-ai-to-revolutionize-engineering-simulation\/","title":{"rendered":"Harnessing The Power of AI to Revolutionize Engineering Simulation"},"content":{"rendered":"

Will generative AI finally unleash the “smartphone moment” for industry?\u00a0<\/strong><\/span><\/h2>\n

For years, engineering simulations have been integral to product development, enabling the creation of better products faster by incorporating simulations early and throughout the design cycle. This approach optimizes, accelerates, and enhances product development.<\/h4>\n

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The Evolution of Engineering Simulation<\/strong><\/span><\/h3>\n

For some products, multiple physics simulations are essential for design improvements and real-time performance prediction. Multi-physics simulations, which couple various physics, are crucial for designing optimized components and systems. A good example is designing electric machines<\/strong>, where optimized electromagnetic performance and structural and thermal integrity are all interrelated.<\/p>\n

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The AI Paradigm Shift<\/strong><\/span><\/h3>\n

What can AI offer engineering simulations? The answer is, a lot!<\/p><\/blockquote>\n

The advent of AI, particularly deep learning neural networks, suggests an impending paradigm shift. These models, commonly used in computer vision, are trained with simulation or test data. They are physics-agnostic and rely on shapes rather than parameters, making them versatile and powerful tools for engineering simulations.<\/p>\n

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