Korea Advances Physical AI Across Industries With NVIDIA
Korea Advances Physical AI Across Industries With NVIDIA
NVIDIA is collaborating with major Korean industry leaders to integrate accelerated computing, AI physics, and simulation into real-world production. Samsung Electronics is using these tools for semiconductor engineering and materials research, achieving significant speedups in thermal analysis and quantum chemical calculations. Kakao Mobility is developing a level 4 autonomous driving system by combining its proprietary software with NVIDIA's vehicle computing platform and safety technologies. LG Electronics has demonstrated robotic manipulation tasks using the Isaac GR00T model on its CLOiD robot within an actual manufacturing environment, establishing a data flywheel for continuous robot learning and validation.
The article reports on NVIDIA's collaborations with Samsung, Kakao Mobility, and LG Electronics to advance physical AI applications in semiconductor manufacturing, autonomous driving, and robotic manipulation within Korea, citing specific technologies like cuISS, Isaac GR00T, and DRIVE AGX Thor.
Source: NVIDIA — Robotics · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
simulation time
41 seconds
View original evidence
ical 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
Open source S5Reported duration
≈18 hours
View original evidence
ical 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
Open source S5speedup factor
1,600
View original evidence
ical 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
Open source S5core count
32
View original evidence
ical 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
Open source S5
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
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 d
Open source S8
ical 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
Open source S5
Source：NVIDIA — Robotics · blogs.nvidia.com