Physics informed neural networks

Jul 17, 2026

AI Can't Learn The Way Humans Do - This Could Fix That

This discussion explores world models as a promising path to solving sample efficiency in AI and achieving AGI. It contrasts deterministic control (Newtonian physics) with stochastic environments (RL), explaining the challenges posed by large action spaces in Go, self-driving, and robotics. The episode delves into how synthetic data, video diffusion models, and latent space architectures like JEPA are making world models practical, while also highlighting remaining open problems in physics modeling, real-time adaptation, and rich sensory integration.

Apr 08, 2026

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.