NVIDIA PhysicsNeMo compatible GPU workstation

One of the current demands for AI is "physical AI," and I believe there are increasing opportunities to hear related terms such as "Omniverse" and "Isaac Sim." This time, I would like to unravel the current role and position of "Omniverse."

On July 2, 2026, I visited the "Monozukuri World Physical AI Exhibition" being held at Tokyo Big Sight. The evolution of generative AI and reinforcement learning is breaking out of the digital world and exploring how to intelligently operate robots in the real world (physical). This exhibition was a very vibrant space that truly hinted at the next generation of manufacturing and the automation of R&D (research and development). I will report on the latest trends that particularly caught my eye on-site!

What is the key to dramatically improving development speed in R&D? The R&D (Research and Development) in manufacturing and mobility development is currently undergoing a significant transformation. With the increasing performance and complexity of products, the amount of data and the computation time required for simulations have exploded. In traditional CAE, even with the use of high-performance HPC, it was necessary to spend enormous computational resources and lead time to obtain high-precision analysis results, taking "days to weeks for a single computation," which has become a major bottleneck in the design cycle. In this "AX (AI Transformation) era," the key to dramatically accelerating the speed of research and development is the "Surrogate Model AI." This article will explain the technological breakthroughs that AI-driven simulations bring to real design and the specific approaches for their implementation.

In the manufacturing industry and advanced research fields, the use of real machines for trial and error is already reaching its limits. NVIDIA's "Physical AI Platform" aims to completely eliminate the heavy constraints of "time, cost, and risk," elevating development speed to an entirely new dimension. This approach, known as "Sim2Real," allows for safe and instantaneous learning in virtual spaces, conducting tens of thousands to hundreds of millions of iterations, and seamlessly implementing the results into the real world. This is not just a concept; it is a powerful ecosystem that has already been put into practical use. In this article, we will elucidate the three phases that make up this platform and the roles of specific libraries that hold the key to its implementation.