This webinar will cover:
- The four layers of an engineering AI architecture, and where agents, solvers, physics AI and governance each sit
- Where physics AI fits: solvers give ground truth, and fast prediction is trained on that data
- What changes when engineering agents run for hours instead of answering prompts, and how that shortens the wait between a design decision and knowing whether it was right
- What it takes to run this for real, from open integration to choosing where it is deployed
By joining this webinar you will learn:
- Why the delay between committing to a design and validating it is where cost and design quality are lost, and how engineering AI targets it
- How to assess any engineering AI stack against a clear reference architecture
- Where NVIDIA technology fits in each layer
- The questions to ask before you start
