Discover how Model-Based Systems Engineering optimizes modern datacenter compute, power and cooling infrastructure. Don't miss out!
Behind every AI breakthrough is a massive physical infrastructure—one of the most complex engineered systems ever built. Modern AI datacenters are not IT projects. They are system-of-systems engineering challenges where architecture decisions ripple across energy consumption, performance, scalability, and sustainability.
This seminar examines how Model-Based Systems Engineering applies to the design and optimization of AI datacenter infrastructure. The talk covers the convergence of high-performance compute, advanced networking, power delivery, and thermal engineering that makes large-scale AI possible. It addresses concrete questions: How do we scale compute without unsustainable energy growth? How do cooling architectures evolve for extreme hardware density? How do engineers coordinate across disciplines when every subsystem affects every other?
Traditional document-based engineering breaks down at this scale. The seminar explores how MBSE introduces system-level thinking through shared digital models—enabling teams to visualize architecture relationships, simulate performance impacts, and maintain traceability from concept through deployment. Drawing on real-world experience from AI infrastructure engagements, the talk provides industry perspectives on architecture-driven engineering, virtual twin approaches, and the cross-domain integration challenges that define modern datacenter design.
