Stop Running CAE Like It’s 2008: Physics+AI Goes Production-Grade

The conversation delves into the founding of EMMI AI and Key Ward, the distinction between physics-based and surrogate models, the impact of AI on engineering workflows, the integration of AI in product development, and the future of AI in engineering. The conversation delves into the future of AI in engineering, the digital maturity of enterprises, the impact of AI on enterprise transformation, and the agility and speed in AI development. It explores the differentiation of AI in physics and generative design, skepticism about AI development, challenges in creating universal engineering models, compressed timeframes for design and simulation, integration of engineering disciplines, assessment of AI maturity in companies, progress from level one to level five in AI adoption, challenges and advancements in AI adoption, validation of AI catalyst for enterprise transformation, customer epiphanies and aha moments with AI adoption, competitive advantage of AI-native startups over big players, comparison of agility and speed in AI development, advantages of AI-native startups over big players, and customer experience and satisfaction with AI-native solutions.

Takeaways

• AI's impact on engineering workflows and development processes
• The distinction between physics-based and surrogate models AI in engineering requires domain-specific models
• AI adoption in enterprises progresses from level one to level five
• AI-native startups have a competitive advantage over big players
• Agility and speed in AI development are key differentiators

Chapters

• 00:00 Introduction and Background
• 06:21 AI-Powered Workflow and Development
• 13:46 AI's Impact on Agile and Development Processes
• 19:12 AI Integration in Key Ward Products
• 25:23 Building Large Engineering Models
• 32:00 The Future of AI in Engineering
• 43:10 Digital Maturity in Enterprises
• 48:26 Impact of AI on Enterprise Transformation

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