NVIDIA PhysicsNeMo compatible GPU workstation

With the spread of generative AI, including ChatGPT, AI has begun to be utilized in various scenarios such as text generation, summarization, translation, and programming assistance. On the other hand, in corporate and research and development settings, there is an increasing expectation for more advanced AI applications, not just to answer questions, but to: - Automatically search for necessary information from internal technical documents - Compare multiple pieces of information to determine the next course of action - Operate external tools and business systems - Progress through a series of tasks including research, analysis, code generation, and report creation This system, which infers based on objectives and utilizes necessary information and tools to advance tasks, is referred to as "Agentic AI." Supporting this Agentic AI is NVIDIA's open model family, known as NVIDIA Nemotron. In this article, we will introduce what NVIDIA Nemotron is, how it differs from traditional generative AI, and what applications can be considered in manufacturing and research and development.

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.