NVIDIA CEO 黄仁勋:一切都将由虚拟孪生表示

📄 中文摘要

在2026年3DEXPERIENCE World大会上,NVIDIA创始人兼CEO黄仁勋(Jensen Huang)大胆预测:‘一切都将被虚拟孪生所代表。’这一宣言源于NVIDIA与法国软件巨头达索系统(Dassault Systèmes)CEO Pascal Daloz的战略合作公告,旨在构建共享工业AI架构,将虚拟孪生技术与基于物理的AI深度融合,重塑设计、工程和制造领域的未来。 虚拟孪生(Virtual Twin)是核心技术,指通过高保真数字模型实时镜像物理实体的动态行为。

📄 English Summary

Everything Will Be Represented in a Virtual Twin, NVIDIA CEO Jensen Huang Says at 3DEXPERIENCE World

[Everything Will Be Represented in a Virtual Twin, NVIDIA CEO Jensen Huang Says at 3DEXPERIENCE World](https://blogs.nvidia.com/blog/huang-3dexperience-2026/) At the 2026 3DEXPERIENCE World conference, NVIDIA founder and CEO Jensen Huang proclaimed, "Everything will be represented in a virtual twin," heralding a transformative era for industry. This bold vision stems from a landmark partnership between NVIDIA and Dassault Systèmes, announced alongside CEO Pascal Daloz, to co-develop a shared industrial AI architecture. By merging virtual twins—high-fidelity digital replicas of physical assets—with physics-based AI, the collaboration aims to revolutionize design, engineering, and manufacturing workflows. Technically, virtual twins evolve beyond traditional data-driven models into dynamic, predictive simulations powered by NVIDIA's physics-based AI. This leverages generative AI models trained on vast physical datasets to emulate real-world laws like fluid dynamics, structural mechanics, and thermodynamics with unprecedented accuracy. NVIDIA's Omniverse platform serves as the backbone, utilizing Universal Scene Description (USD) for interoperable 3D data exchange, enabling real-time collaboration across tools like Dassault's 3DEXPERIENCE suite. Key innovations include the integration of NVIDIA's Cosmos physics foundation models, which generate optimized designs from initial sketches—e.g., iteratively refining aerodynamics for vehicles—and RTX-accelerated ray tracing for photorealistic rendering at scale. Edge AI extensions via Jetson modules allow factory-floor twins for real-time anomaly detection, fusing sensor data with simulations. The architecture's novelty lies in its open, scalable framework: PB-scale simulations run on NVIDIA DGX systems or cloud, reducing physical prototyping by 70-90%. Reinforcement learning optimizes processes, such as robotic assembly paths, while generative AI automates iterative design, slashing time-to-market. Applications span sectors.

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