Agentic Engineering & PINNs: AI for Simulation Engineers - James Shaw | Podcast #172
James Shaw, a mechanical engineer and Ansys channel partner, delves into the current and future impact of agentic AI and physics-informed neural networks (PINs) on simulation workflows. He explores how AI is revolutionizing aspects from tech support and model setup to the solver itself, particularly in CFD. The discussion also covers the implications for the engineering job market, the 'senior-junior inversion crisis', and the continued irreplaceability of skilled engineers due to the inherent physicality of the world, emphasizing the need for robust, trustworthy data to train AI.
James Shaw, a mechanical engineer and Ansys channel partner, discusses the evolving landscape of agentic AI within simulation workflows, particularly for Mechanical Engineering. He begins by tracing the evolution of software from Software 1.0 (manual coding) to Software 2.0 (weights and biases predicting outcomes) and Software 3.0 (Large Language Models using natural language input for various creations, including CAD models). Shaw notes that Software 3.0’s adoption is unique, with masses adopting it first before large corporations.
Agentic AI in Engineering Workflows
Shaw draws a parallel between the current AI revolution and the early days of simulation adoption 20-25 years ago. He highlights that early adopters of simulation, often young engineers, faced skepticism from experienced colleagues, similar to how AI is perceived today. The deployment of AI in engineering workflows is envisioned in several steps:
- Agentic for Technical Support: This is the current,