COVER STORY - AGE OF AI
Physics-inspired AI is transforming automotive, manufacturing, aerospace and energy
6 years, we have done many AI for chip design research projects and several of these are in production use today. Our projects cover a wide spectrum of design areas, such as architecture design, RTL, verification, synthesis, cell design, physical design – including placement, route and optimisation – as well as analogue design and lithography.”
INDUSTRY OUTLOOK Generative AI is rapidly finding its feet in a wide range of industrial sectors, Linford says. For instance, streamlining vehicle design and manufacturing in the automotive industry, powering a new wave of healthcare innovation through drug discovery and enhancing the operational efficiency of telecommunications. “Physics-inspired AI is
AI DESIGN TOOL ADVANCEMENTS The capabilities of generative AI – the ability of algorithms to create new text, images, sounds, animations, 3D models and computer code – are moving at incredible speed. By employing large language models (LLMs), the technology can substantially reduce the time people devote to manual tasks like searching for and compiling information, for instance. Within more industry-focused
scenarios, “Generative AI is changing the game in semiconductor manufacturing, for instance LLMs enhancing code generation in the design space,” Linford offers. “For example, NVIDIA uses ChipNeMo, a domain specific foundation model trained on NVIDIA data, to help our internal design work. Engineering assistant copilots are helping design chips, design systems and optimise fab operations. Agentic workflows combine multiple AI agents to execute complex tasks, like bug summarisation and analysis on multi-modal data such as text, images and video.” He continues, “At NVIDIA, we build AI to build chips for AI! Over the last
transforming the automotive, manufacturing, aerospace and energy industries,” he continues. “For example, Siemens Gamesa used the NVIDIA PhysicsNeMo framework to train a PINN that led to 4,000 times faster wind turbine wake optimisation compared with traditional approaches. This speed increase enables large-scale, detailed wind farm layouts that optimise turbine placement and maximise energy output.” Real-time digital twins (RTDTs)
are the cutting edge of computer- aided engineering (CAE) simulation, because they enable immediate feedback in the engineering design loop. RTDTs have soared in demand within the aerospace, automotive and electronic design industries in particular. “Siemens Energy uses digital
twins to maximise uptime for heat recovery steam generators,” Linford says. “These massive machines use hot exhaust gases to boil water. The exhaust gases can cause corrosion, leading to downtime for system maintenance. High-fidelity simulations of multiphase turbulent flow help predict where and when corrosion occurs. Physics-informed AI can infer this flow in seconds, making these simulations feasible. With simulation, unplanned downtime is reduced by up to 70%, saving the industry $1.7 billion per year.
John Linford, principal technical product manager at NVIDIA
A LOOK INTO THE FUTURE According to Linford, generative AI will have a pivotal role to play in how design engineering processes of the future will operate. “Advanced warehouses and factories
use fleets of hundreds of autonomous mobile robots, robotic arm manipulators and humanoid robots working alongside people,” he says. “Implementations of increasingly complex systems of sensor and robot autonomy require coordinated AI training in simulation to optimise operations, help ensure safety and avoid disruptions.” And what about from NVIDIA’s
perspective? “NVIDIA offers enterprises a reference architecture of NVIDIA accelerated computing, AI NVIDIA Isaac and NVIDIA Omniverse technologies to develop and test digital twins for A-powered robot ‘brains’ that drive robots, video analytics AI agents, equipment and more for handling enormous complexity and scale,” he offers. “This framework brings software-defined capabilities to physical facilities, enabling continuous development, testing, optimisation and deployment.” “Accelerated computing enables AI,
and AI enables quantum computing. AI models trained on quantum data generated from simulators and physical hardware are expected to unlock useful quantum computing. Broad adoption of AI ultimately leads to data generation and model training at scales that were previously impossible, transforming industries and enabling new opportunities.”
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