Korea Advances Physical AI Across Industries With NVIDIA
Korea Advances Physical AI Across Industries With NVIDIA
NVIDIA announced progress on physical AI collaborations with multiple Korean companies. Samsung is using NVIDIA cuEST for quantum chemistry computations on its sMOL platform, delivering up to 5x end-to-end speedup, while one AI homogenization case achieved roughly 1,600x speedup by using AI to predict material properties, coarse meshes, and GPU acceleration, cutting simulation time from about 18 hours on 32 CPU cores to 41 seconds.
Tapping into the country’s strengths in manufacturing and engineering, NVIDIA is helping advance Korea’s physical AI ecosystem.
The country’s industry leaders are applying accelerated computing, AI and simulation to everything from semiconductor manufacturing to robotics and autonomous vehicles.
These efforts are helping bring physical AI from development into real-world production.
Samsung Advances Semiconductor Engineering With Accelerated Computing and AI Physics
Samsung Electronics is expanding its collaboration with NVIDIA to advance semiconductor innovation through accelerated computing and AI physics. The work spans materials research, process development and device engineering.
In technology computer-aided design, Samsung is working with NVIDIA cuISS and NVIDIA cuDSS to accelerate computations involving sparse linear systems. The collaboration also extends to GPU acceleration for process and optical simulations. In materials research, Samsung’s sMOL platform has run selected quantum chemical calculations up to 5x faster end to end with NVIDIA cuEST. Samsung is also exploring NVIDIA cuEquivariance to speed up tensor operations used in training and inference for atomistic AI models.
Samsung is developing chip-scale thermal and mechanical analysis workflows with NVIDIA PhysicsNeMo. In one AI homogenization case, combining AI-predicted effective material properties, coarser grids and GPU acceleration resulted in a roughly 1,600x speedup. Simulation time fell from about 18 hours for a fine-grid solve on a 32-core CPU to 41 seconds for a GPU-accelerated coarse-grid solve.
Looking ahead, NVIDIA Agent Toolkit could connect these simulation tools and AI models, helping future engineering agents run analyses and compare design options.
Kakao Mobility Collaborates With NVIDIA to Advance Autonomous Driving Development
Kakao Mobility plans to build on NVIDIA’s autonomous vehicle computing and safety technologies to advance development of a level 4 autonomous driving system. The work combines Kakao Mobility’s proprietary driving software and experience operating in Seoul’s Gangnam district with NVIDIA’s vehicle computing platform.
Kakao Mobility plans to use NVIDIA Hyperion, a reference platform for vehicle sensors and compute, powered by NVIDIA DRIVE AGX Thor. It also plans to use NVIDIA Halos to support safety across the development and validation of its autonomous driving technology.
Initial development is planned around Kia purpose-built vehicles, integrating Kakao Mobility’s autonomous driving software with NVIDIA technologies. The effort aims to support future level 4 mobility services in Korea.
LG Electronics Advances Robot Learning and Factory Simulation
NVIDIA and LG Electronics successfully demonstrated NVIDIA Isaac GR00T-powered robotic manipulation and manufacturing tasks using LG’s CLOiD robot in an actual manufacturing environment.
The collaboration implemented a physical AI data flywheel connecting real-world data collection, NVIDIA Cosmos-based data augmentation, GR00T model training, NVIDIA Jetson AGX Thor onboard computing and simulation-based evaluation, establishing reusable workflows for continued development and validation of CLOiD’s robotic capabilities.
The teams are using this work to refine a repeatable process for training, evaluating and improving robots for manufacturing.
Source: NVIDIA Blog · blogs.nvidia.com