AI use cases in China
3 documented implementations
GAC R&D Center sets record-low vehicle drag coefficient using NVIDIA GPU-accelerated CFD simulation
Guangzhou Automobile Group Co., Ltd. (GAC Group)
GAC R&D Center (GAC Group) deployed NVIDIA V100 SXM2 Tensor Core GPUs on its hybrid cloud HPC platform, using Altair's ultraFluidX CFD software for GPU double-precision computing, to design a new concept car. In less than six months the team completed over 200 transient CFD simulations of vehicle outflow, achieving a simulation drag coefficient of 0.147 (wind tunnel test value 0.146 at Tongji University's Shanghai Automotive Wind Tunnel Center), a new record versus the previous 0.19. Compared to conventional CFD approaches, manual modeling effort was reduced by nearly 60% and total simulation time was shortened by about 70%.
United Imaging Healthcare Enhanced the Speed and Quality of MR Imaging
United Imaging Healthcare
United Imaging Healthcare worked with NVIDIA since 2018 to accelerate its magnetic resonance (MR) imaging products with AI, developing an AI-assisted compressed sensing (ACS) technology to speed up MR image acquisition and reconstruction. Using NVIDIA data center GPUs, the CUDA Toolkit, cuSolver matrix computation library, and NVIDIA AI Enterprise software (with support from NVIDIA DevTech engineers and solution architects), United Imaging Healthcare reduced image reconstruction time by nearly 95%, with NVIDIA's support teams further accelerating algorithms by an additional 3x, on top of an initial 10x increase in computational speed from a tailored algorithm.
Ping An's AI-Driven Car Insurance Claims Processor
Ping An
Ping An launched a credit-based mobile app for car insurance claims, letting drivers report claims, upload photos for AI-assisted damage assessment, and receive compensation online. The system is built on an AI smart claims credit model using image damage assessment, OCR bill identification, and biometrics. Smart scheduling provides surveying within 5-10 minutes for 95.5% of claims, 30% of cases use AI image recognition for loss assessment with 95% accuracy, and compensation can be received within seconds via face recognition.