# What Is a Digital Twin?

Digital twins are virtual representations of products, processes, and facilities that enterprises use to design, simulate, and operate their physical counterparts.

## How Do Digital Twins Work?

Digital twins are born by integrating data that best describes their real-world counterparts. These data sources and formats vary based on the type of digital twin, industry, and use case, but are typically composed of 1D (e.g., tabular data from IT/OT systems) and 2D/3D (e.g., CAD, reality capture scans, BIM) data. Combining these data sources to create digital twins unlocks incredible new possibilities, from advanced design and planning to simulation (using [SimReady](https://www.nvidia.com/en-us/glossary/simready.md) assets) and remote monitoring and control of operations. Internet of Things (IoT) sensors and devices play a crucial role in providing real-time data that keeps digital twins accurate and up-to-date, allowing for dynamic interactions between the physical and digital realms.

## The Evolution of Digital Twin Technology

NASA is widely recognized for pioneering the concept of digital twins, a revolutionary idea demonstrated by the Apollo 13 mission. During this mission, NASA utilized Earth-based simulators connected to the spacecraft via real-time data updates, which allowed engineers to troubleshoot alongside astronauts and ultimately avert a disaster.

While the concept of digital twins has been applied in [industrial manufacturing](https://www.nvidia.com/en-us/industries/industrial-sector.md) since the early 2000s, recent advancements are pushing the boundaries of digital twin technology even further. Digital twins are now benefiting from improvements in data interoperability driven by open data frameworks like [OpenUSD](https://www.nvidia.com/en-us/omniverse/usd.md), computer graphics, generative AI, and accelerated computing—leading to the emergence of a new class of physically based, AI-enabled digital twins.

These next-generation digital twins not only connect to enterprise data and production systems at the edge but also incorporate physically accurate materials, lighting, rendering, and behavior to support a range of advanced planning, simulation, and operational use cases.

As digital twins evolve, they become crucial in testing and refining the generative physical AI driving autonomous systems in the real world.

This technological leap enables more precise optimizations in workflows, enhances customer experience, and improves decision-making by aggregating historical and operational data. In turn, digital twin technology facilitates predictive maintenance, reduces downtime, minimizes physical or material waste, boosts product quality, and enables supply chain optimization.

Digital transformation, driven by digital twin technology, is setting new standards for product and facility lifecycle management and automation—ensuring that physical objects and their digital versions are optimally aligned and efficiently managed throughout their lifecycle.

Siemens

Caption: Digital twin of a ship visualized in Siemens TeamCenter X powered by NVIDIA Omniverse™ APIs

### Digital Twins for Physical AI

Get started with essential foundations in developing OpenUSD-based digital twin applications and workflows for the era of physical AI.

[Get Started](https://www.nvidia.com/en-us/learn/learning-path/digital-twins.md)

Quick Links

[Accelerate AI Factory Buildouts](https://nvidianews.nvidia.com/news/nvidia-releases-vera-rubin-dsx-ai-factory-reference-design-and-omniverse-dsx-digital-twin-blueprint-with-broad-industry-support)

[Industrial Facility Use Cases](https://www.nvidia.com/en-us/use-cases/industrial-facility-digital-twins.md)

[What Is Industrial AI?](https://www.nvidia.com/en-us/glossary/industrial-ai.md)

## What Are the Benefits of Digital Twins?

Digital twins are foundational to industrial digitalization and offer numerous benefits to enterprises across all industries. These include:

### Streamlined Design and Planning Process

Digital twins streamline communication for project stakeholders, allow teams to visualize and quickly make decisions in full context, and ensure that decisions are informed by the most current data. For example, [BMW Group](https://www.nvidia.com/en-us/case-studies/bmw-group-develop.md) uses digital twins of their factories to speed up greenfield factory planning—resulting in expected efficiency gains of up to 30%.

### Simulation Capabilities

Simulations are essential to realizing the full potential of digital twins—allowing teams to safely predict, validate, and optimize real-world performance in a virtual environment. Teams can simulate anything, from process and layout changes to robot fleet and airflow simulation.

[Wistron](https://developer.nvidia.com/blog/wistron-advances-energy-efficiency-in-manufacturing-with-ai-and-nvidia-omniverse/), one of the world’s largest suppliers of information and communications products, uses digital twins to speed up airflow simulations—reducing a process that previously took its teams 15 hours to just 3.6 seconds, a 15,000x speedup.

### Optimization of Operations

By [connecting digital twins to real-time operational systems and production data](https://developer.nvidia.com/blog/connect-real-time-iot-data-to-digital-twins-for-3d-remote-monitoring/) from IoT devices and sensors at the edge, teams can remotely monitor operations to identify, analyze, and resolve issues. Operations teams also infuse AI into their digital twins to train computer vision models for defect detection in the real world.

[Pegatron](https://developer.nvidia.com/blog/pegatron-simulates-and-optimizes-factory-operations-with-ai-enabled-digital-twins/), for example, adopts AI-enabled digital twins to catch up to 60% more defects with 30% fewer variations than human inspectors.

### Bringing Industrial AI and Physical AI to Facilities

[Industrial AI](https://www.nvidia.com/en-us/glossary/industrial-ai.md) and [physical AI](https://www.nvidia.com/en-us/glossary/generative-physical-ai.md) will transform heavy industries and bring more intelligence, automation, and autonomy to industrial facilities. Digital twins are critical proving grounds for these AIs—enabling enterprises to test and verify advanced industrial AI models in simulation before deploying them in the real world.

### Cost Savings

Through predictive maintenance, optimized operations, and reduced physical prototyping—digital twins can lead to significant cost savings across product and facility lifecycles.

## What Skills Are Needed to Develop Digital Twins?

Building a team with the right mix of roles and skills is key to successful digital twin development. While skills and roles might change based on industry and use case, teams are typically made up of a mix of developers, 3D experts, and technologists with the following skills:

* Developers: Experience with Python, React, and UI/UX design.
* 3D Experts: Experience with CAD, BIM, OpenUSD, materials, lighting, physics, and animation.
* Technologists: Experience with IT/OT systems integration, data center networking, AI/machine learning, DevOps, and data architecture.

These core teams are often supported by systems integrators and software development and delivery partners like [Accenture](https://newsroom.accenture.com/news/2024/accenture-and-nvidia-lead-enterprises-into-era-of-ai), [SoftServe](https://developer.nvidia.com/blog/spotlight-continental-and-softserve-deliver-generative-ai-powered-virtual-factory-solutions-with-openusd/), and [T-Systems](https://www.t-systems.com/de/en/industries/automotive/solutions/industrial-metaverse-with-nvidia-omniverse).

Explore the [digital twins learning path](https://www.nvidia.com/en-us/learn/learning-path/digital-twins.md) to get started with essential foundations in developing OpenUSD-based digital twin applications and workflows for the era of physical AI.

## What Are Some Digital Twin Use Cases?

Digital twins are being used to support a range of design and planning, simulation, and operations use cases. Examples across industries are included below:

### Industrial Facility Digital Twins

The age of [physical AI](https://www.nvidia.com/en-us/glossary/generative-physical-ai.md), with [embodied AIs](https://www.nvidia.com/en-us/glossary/embodied-ai.md) interacting with industrial facilities, is approaching. This will bring increased intelligence, automation, and autonomy to factories and warehouses worldwide. Physics-based industrial digital twins are crucial for this transformation, bridging the physical and digital worlds. These [virtual facilities](https://blogs.nvidia.com/blog/virtual-factories-industrial-digitalization/) act as the birthplace and testing ground for intelligent facilities, [robotics simulations](https://www.nvidia.com/en-us/use-cases/robotics-simulation.md), and multi-robot fleets.

[Developers at Continental](https://developer.nvidia.com/blog/spotlight-continental-and-softserve-deliver-generative-ai-powered-virtual-factory-solutions-with-openusd/) created [ContiVerse](https://www.nvidia.com/en-us/on-demand/session/gtc24-s62919/), a factory planning and manufacturing operations application, which leverages OpenUSD and [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse.md) libraries. The application helps Continental’s planning and operations teams optimize factory layouts and plan production processes collaboratively, leading to an expected 10% reduction in maintenance effort and downtime.

[Rockwell Automation](https://www.nvidia.com/en-us/customer-stories/rockwell-automation.md) is enhancing factory design, testing, and commissioning with its [Emulate3D Factory Test](https://www.rockwellautomation.com/en-us/products/software/factorytalk/designsuite/emulate3d-digital-twin.html) platform, which enables manufacturers to build factory-scale, physics-based digital twins. This advanced simulation capability boosts project win rates by 50% and reduces time-to-market from years to months.

[Foxconn is building digital twins of factories](https://www.nvidia.com/en-us/case-studies/foxconn-develops-physical-ai-enabled-smart-factories-with-digital-twins.md) to optimize layouts, configurations, and equipment placement, significantly reducing the cost of physical changes and enhancing operational efficiency. For [Foxconn’s new state-of-the-art facility in Houston, Texas,](https://nvidianews.nvidia.com/news/nvidia-us-manufacturing-robotics-physical-ai) engineers are using Siemens’ digital twin tech stack, which is developed on NVIDIA Omniverse libraries , to assemble and validate every mechanical, electrical, and plumbing system virtually before construction.

This factory-born digital approach then uses the [“Mega” NVIDIA Omniverse Blueprint](https://build.nvidia.com/nvidia/mega-multi-robot-fleets-for-industrial-automation) and open-source NVIDIA Isaac Sim framework to design, simulate, train, and validate fleets of AI-powered robots that will work alongside factory employees manufacturing NVIDIA AI infrastructure systems. This approach enables the company to train and test AI applications for robotic tasks in a virtual environment, ensuring accurate implementation and improved performance in real-world operations.

![NVIDIA Omniverse, Isaac™, and Metropolis bring the power of AI robots to Foxconn’s factory digital twin.](https://www.nvidia.com/content/dam/en-zz/Solutions/case-studies/digital-twin/gtcdc25-foxconn-demo-yt-ari.jpg)

Consent for Optional Cookies

(googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information.

Cancel

Accept and Play Video

Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink).

Caption: NVIDIA Omniverse, Isaac™, and Metropolis bring the power of AI robots to Foxconn’s factory digital twin.

[Sight Machine](https://www.nvidia.com/en-us/customer-stories/sight-machine.md) delivers integrated industrial AI solutions that help manufacturers monitor operations, quickly resolve production issues, and reduce downtime. Its Operator Agent leverages live production data, AI-driven recommendations on Microsoft Azure, and accurate digital twins built with OpenUSD and NVIDIA Omniverse technologies. By unifying data and streamlining workflows, it delivers real-time visibility and context—enabling faster, smarter decisions that improve productivity, throughput, and profitability.

### Product Development

Digital twins are increasingly being used for product design and engineering reviews. They accelerate virtual prototyping and design iterations, allowing designers and engineers to explore different design options without the need for expensive physical prototypes. These digital replicas of physical products are used to run complex simulations to test various scenarios, predict performance, and optimize designs.

[Siemens Teamcenter X](https://www.youtube.com/watch?v=rrb2tPHiLRo) uses NVIDIA Omniverse APIs to enable designers and engineers to create [immersive and photorealistic digital twins](https://blogs.nvidia.com/blog/siemens-immersive-visualization-generative-ai/). Engineers can navigate, edit, and iterate on shared virtual models in real time, facilitating collaboration and reducing errors. With physically accurate models and real-time updates, Teamcenter X empowers users to validate designs, minimize workflow waste, and save time and costs on industrial-scale projects.

Real-time digital twins are at the cutting edge of [computer-aided engineering (CAE)](https://www.nvidia.com/en-us/solutions/cae.md) simulation and are in high demand across the manufacturing industry—from aerospace to automotive and electronics design. They provide engineers with immediate feedback in the engineering design loop and enable them to innovate freely and rapidly explore new designs for cars, airplanes, ships, and many other products.

The [NVIDIA Omniverse Blueprint for interactive fluid simulation](https://developer.nvidia.com/blog/rapidly-create-real-time-physics-digital-twins-with-nvidia-omniverse-blueprints/) enables developers to create digital twins by combining accelerated solvers, simulation AI, and virtual environments. Industry-leading software developers like [Ansys](https://www.nvidia.com/en-us/industries/industrial-sector/ansys.md), [Cadence](https://www.nvidia.com/en-us/industries/industrial-sector/cadence.md), and [Siemens](https://www.nvidia.com/en-us/industries/industrial-sector/siemens.md) can use the blueprints to develop CAE software tools that enable real-time visualization and analysis of products as they develop. [Luminary Cloud](https://www.luminarycloud.com/), a member of the [NVIDIA Inception](https://www.nvidia.com/en-us/startups/?ncid=cont-990340-vt33) program for startups, leveraged the NVIDIA Omniverse Blueprint with its cloud-native, GPU-accelerated solver to realize a real-time virtual wind tunnel. Rescale is also incorporating the blueprint into its physics-AI platform, enabling real-time digital twins for industry software developers.

### Product Configurators

Auto companies, retailers, and [CPG companies](https://blogs.nvidia.com/blog/retail-agentic-physical-ai/) are developing [3D product configurators](https://www.nvidia.com/en-us/use-cases/3d-product-configurator/#:~:text=A%203D%20product%20configurator%20is,render%20them%20in%20photorealistic%20quality.) to deliver engaging experiences and content at scale using entirely digital products and environments instead of physical assets. These product digital twins can enable non-3D artists to create and customize photorealistic, personalized 3D content for marketing campaigns, reducing costs and content production times by repurposing datasets and automating repetitive tasks with generative AI.

[Unilever and Collective World’s content](https://www.nvidia.com/en-us/on-demand/session/gtc25-s71412/) engine and the [Grip platform utilized by Moët Hennessy](https://www.nvidia.com/en-us/customer-stories/grip.md) showcase how global brands are streamlining production with real-time 3D rendering, automated asset generation, and AI-driven consistency checks—ensuring every visual is brand-accurate, photorealistic, and ready for market at speed and scale.

[Katana,](https://resources.nvidia.com/en-us-new-media-entertainment/katana-studio-streamlines) a CGI studio, enables marketing teams at Nissan to create campaign assets on demand from 3D data through its user-friendly content creation application.

To take this experience to the next level,developers are taking advantage of the [spatial streaming workflow guide](https://docs.omniverse.nvidia.com/avp/latest/index.html) to build solutions that [stream interactive digital twins to the Apple Vision Pro](https://blogs.nvidia.com/blog/omniverse-apple-vision-pro/), allowing consumers to enter immersive worlds and enter the vehicle in [extended reality (XR)](https://www.nvidia.com/en-us/design-visualization/solutions/virtual-reality.md).

With NVIDIA NIM™ microservices, [USD Search](https://docs.omniverse.nvidia.com/services/latest/services/usd-search/overview.html), and [USD Code](https://docs.omniverse.nvidia.com/services/latest/services/usd-code/overview.html), marketing leader [WPP](https://www.nvidia.com/en-us/industries/media-and-entertainment/wpp.md) is enabling [The Coca-Cola Company](https://blogs.nvidia.com/blog/coca-cola-wpp-omniverse-generative-ai/) to accelerate iteration on creative campaigns at a global scale.

Developers at independent software vendors (ISVs) and production services agencies—such as [Accenture Song, Collective World, GRIP, Monks, and WPP](https://developer.nvidia.com/blog/building-a-generative-ai-openusd-app-for-brand-accurate-marketing-visuals/)—are building the next generation of content creation solutions, infused with controllable generative AI, built on OpenUSD and the [3D conditioning for precise visual generative AI](https://build.nvidia.com/nvidia/conditioning-for-precise-visual-generative-ai) Omniverse Blueprint.

### Architectural Design and Simulation

Building design teams face a growing demand for efficient collaboration, faster iteration on renderings, and expectations for accurate simulation and photorealism. These demands can become even more challenging when teams are dispersed worldwide.

Digital twins enable [real-time collaboration with building information modeling](https://developer.nvidia.com/blog/optimizing-bim-workflows-using-usd-at-every-design-phase/) and non-BIM data sources at every design phase. OpenUSD allows building design teams to [integrate their 3D architecture](https://developer.nvidia.com/blog/enhancing-architectural-3d-modeling-collaboration-with-universal-scene-description/) data in a digital twin, enabling users of different tools to collaborate in the same virtual environment.

Leading architecture firm [Zaha Hadid Architects (ZHA)](https://www.nvidia.com/en-us/case-studies/zaha-hadid-architects-with-omniverse-and-usd.md) uses digital twins powered by OpenUSD to enable design teams to collaborate on complex project designs and accelerate iteration cycles.

### Remote Monitoring of Industrial Operations

Industrial enterprises are increasingly integrating AI into their operations to deliver more automated and autonomous facilities. With these shifts, operations teams are becoming centralized in remote operations centers. These teams increasingly embrace digital twins [to monitor operations](https://www.nvidia.com/en-us/omniverse/usd.md), gain deeper insights into systems and facilities, and accelerate problem identification and decision-making.

Microsoft Azure collaborated with NVIDIA to develop a [reference architecture, Azure Arc Jumpstart, and a public GitHub repo](https://developer.nvidia.com/blog/connect-real-time-iot-data-to-digital-twins-for-3d-remote-monitoring/) to help developers build operations digital twins. Developers can leverage these resources to connect 3D models of industrial systems and production environments to real-time data from Azure IoT Operations and Power BI reports.

### Autonomous System Testing and Validation

Autonomous machines, such as [self-driving](https://www.nvidia.com/en-us/self-driving-cars.md) cars and warehouse robots, require vast amounts of sensor data to be adequately trained and prepared for the environments in which they operate.

Digital twins are the birthplace of these physical AI systems. They provide a solution to the data gap that artificial intelligence developers often experience, as they can be used as a safe sandbox to generate [synthetic data](https://www.nvidia.com/en-us/use-cases/synthetic-data/?deeplink=content-tab--1) and train, test, and validate AI models. [Amazon Robotics](https://www.youtube.com/watch?v=LUnZXBL_lqA), for example, uses digital twins of its warehouses to simulate and optimize warehouse design and flow. The company uses these environments to generate large photorealistic synthetic datasets to accelerate training, improve the accuracy of computer vision models, and improve overall productivity. When models are deployed to the real world, warehouse robots can detect objects more effectively and navigate the facility.

In the automotive industry, creating digital twins for simulation is crucial for training, testing, and deploying autonomous vehicles, but achieving real-world fidelity is challenging. The [NVIDIA Omniverse Blueprint for AV simulation](https://www.nvidia.com/en-us/use-cases/autonomous-vehicle-simulation.md) helps address this by enabling large-scale, high-fidelity [sensor simulation](https://www.nvidia.com/en-us/glossary/sensor-simulation.md). With this API-based reference workflow, developers like CARLA, MathWorks, and Foretellix can deliver digital twins and render physically based sensor data for cameras, lidars, and radars, enhancing AV development.

[KION Group](https://blogs.nvidia.com/blog/mega-omniverse-blueprint/) is leveraging [Mega](https://build.nvidia.com/nvidia/mega-multi-robot-fleets-for-industrial-automation), an NVIDIA Omniverse Blueprint for testing multi-robot fleets, to train and test its robotic agents—including intelligent cameras, forklifts, and robotic equipment—in a virtual environment before real-world deployment. By simulating warehouse operations, KION ensures seamless integration, reducing deployment risks and improving operational efficiency.

KION Group, Accenture

NVIDIA Omniverse, Isaac, and Metropolis bring the power of industrial digital twins to industrial warehouses to simulate, test, and optimize robotic fleets at scale.

### Optical Inspection and Defect Detection

[Delta Electronics](https://www.nvidia.com/en-us/case-studies/delta-electronics-industrial-innovation.md), a global leader in power and thermal management technologies, utilizes digital twins to train computer vision and AI-assisted Automated Optical Inspection (AOI) models, enabling the quick detection of defects such as missing components or misaligned screws, thereby reducing the need for manual inspection.

[Pegatron uses NVIDIA Metropolis for Factories](https://developer.nvidia.com/blog/pegatron-simulates-and-optimizes-factory-operations-with-ai-enabled-digital-twins/) to enhance its printed circuit board (PCB) factories with simulation, robotics, and automated production inspection, achieving 99.8% accuracy in defect detection with small datasets.

### Data Center and AI Factory Optimization

Digital twins are revolutionizing the [design and operation of next-generation data centers](https://blogs.nvidia.com/blog/omniverse-next-gen-data-center/) and [AI factories](https://www.nvidia.com/en-us/glossary/ai-factory.md). With OpenUSD, engineers can integrate and visualize CAD datasets with physical accuracy and precision, allowing for simulations of aspects such as airflow and cooling systems. The use of digital twins also enables faster deployment and more efficient, accurate optimization of data center designs, significantly enhancing the planning and execution stages of data center development.

The [NVIDIA Omniverse Blueprint for AI factory digital twins](https://blogs.nvidia.com/blog/omniverse-blueprint-ai-factories-expands) enables developers to build a unified digital twin where all aspects of an AI data center can be designed, simulated, and optimized together before construction begins. Using OpenUSD libraries, engineers can aggregate and visualize 3D data from all facility components, enabling real-time, physically accurate simulations that bring together traditionally siloed teams. This approach allows engineers to instantly test design changes, validate redundancy, and model failure scenarios, significantly reducing risk, saving time, and future-proofing next-generation data center designs.

NVIDIA has also [announced the build-out of an AI Factory Research Center at Digital Realty in Virginia](https://nvidianews.nvidia.com/news/nvidia-partners-ai-infrastructure-america). This facility, powered by the NVIDIA Vera Rubin platform, will accelerate breakthroughs in generative AI, scientific computing and advanced manufacturing and serve as a foundation for pioneering research in digital twins and large‑scale simulation.

The center lays the groundwork for [NVIDIA Omniverse DSX](https://blogs.nvidia.com/blog/omniverse-dsx-blueprint/)—a blueprint for multi‑generation, gigawatt‑scale build‑outs using NVIDIA Omniverse libraries—that will set a new standard of excellence for AI infrastructure. By integrating virtual and physical systems, NVIDIA is [creating a scalable model](https://www.youtube.com/watch?v=Odve87ieiYE) for building intelligent facilities that continuously optimize for performance, energy efficiency, and sustainability.

For logically simulating the AI factory, [NVIDIA Air](https://www.nvidia.com/en-us/networking/ethernet-switching/air.md) is a cloud-based platform that enables the creation of [networking digital twins](https://www.youtube.com/watch?v=0BiPI_WeGSA). NVIDIA Air delivers full simulation of NVIDIA Spectrum-X™ AI fabrics, including Spectrum Ethernet switches, NVIDIA® BlueField® DPUs and SuperNIC™ network accelerators, and the NVIDIA NetQ™ network visibility and telemetry toolset. NVIDIA Air enables comprehensive modeling of both front-end user access networks and back-end GPU-networks, reducing Day 0 and Day 1 deployment time by over 70% and enhancing Day 2 operations by minimizing unplanned downtime.

### Digital Surgery

Healthcare institutions are finding compelling use cases for digital twins in areas such as [surgical preparation](https://blogs.nvidia.com/blog/atlas-meditech-brain-surgery-ai-digital-twins/). Surgeons can rehearse procedures using multimedia tools and then transition to highly realistic simulations with the assistance of digital twins. In [neurosurgery](https://blogs.nvidia.com/blog/atlas-meditech-brain-surgery-ai-digital-twins/), these digital models are customized to match the patient’s brain anatomy, enabling surgeons to practice on virtual brains that accurately replicate the patient’s specific size, shape, and lesion position.

The simulations also employed AI algorithms to suggest safe surgical pathways and predict how brain tissue would respond during the operation. Furthermore, [operating room digital twins](https://youtu.be/EV3UonTD8FE?si=-bt0KWEQB2qPc_dd) allow surgeons to immerse themselves in lifelike environments and receive feedback on their performance.

### Smart Cities: Urban Planning and Operations

Smart cities are transforming how we live by using technology to solve complex urban challenges. By harnessing the power of [video analytics AI agents](https://www.nvidia.com/en-us/use-cases/video-analytics-ai-agents.md) and digital twin technology, cities can gain deep insights into various aspects of urban life, including traffic flow, pedestrian safety, and infrastructure planning. [NVIDIA Blueprint for Smart City AI](https://blogs.nvidia.com/blog/smart-city-ai-blueprint-europe/) empowers city operators to make informed decisions, optimize city designs, and enhance ‌residents’ overall quality of life.

![YouTube Video](https://img.youtube.com/vi_webp/1ocuKybmPw4/maxresdefault.webp)

Consent for Optional Cookies

(googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information.

Cancel

Accept and Play Video

Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink).

Caption: City simulation courtesy of KPF

The emergence of digital twins real-time traffic scenarios enables machine learning engineers to generate synthetic datasets that accurately represent real-world traffic patterns and violations. These synthetic datasets help validate AI models and optimize training pipelines, leading to smart city traffic management systems that reduce congestion, lower emissions, and enhance emergency response and public services.

Digital twins also serve as a powerful, interactive visualization tool for various types of sensor data, including camera data. By combining AI with digital twins, users can query the data in real time to discover insights, proactively handle unexpected events, and reduce overall response times.

By using virtual replicas of cities, [Linker Vision](https://www.nvidia.com/en-us/customer-stories/linker-vision-ai-smart-city-solutions.md) is deploying [NVIDIA Blueprint for smart city AI](https://blogs.nvidia.com/blog/smart-city-ai-blueprint-europe/) to create end-to-end AI solutions—including Omniverse libraries for city-scale digital twins, [NVIDIA Cosmos™ Reason](https://github.com/nvidia-cosmos/cosmos-reason2) for fine-tuning AI models, and [video analytics AI agents](https://www.nvidia.com/en-us/use-cases/video-analytics-ai-agents.md) for real-time awareness to enhance city operations, deliver intelligent event response, and promote cross-departmental collaboration in smart cities like Kaohsiung.

### Wireless Network Simulation

Digital twins enable the simulation of system-level behavior without abstraction, catering to the unique demands of advanced 5G and [6G networks](https://developer.nvidia.com/6g-program). Their detailed 3D models accurately replicate electromagnetic propagation, facilitating ‌stress-testing of numerous cells with large user volumes.

The [NVIDIA Aerial Omniverse Digital Twin](https://developer.nvidia.com/aerial-omniverse-digital-twin) enables accurate simulations of 5G and 6G systems, from single towers to entire cities, incorporating software-defined RAN, user-equipment simulators, and realistic terrain properties. This allows researchers to simulate and build base-station algorithms using site-specific data and train models in real time to enhance spectral efficiency.

![Image of city skyline with simulated wireless connections intersecting the buildings showing a virtual connection.](https://img.youtube.com/vi_webp/J5-rkgL2dFA/maxresdefault.webp)

Consent for Optional Cookies

(googleCookiePolicyLink)YouTube sets performance, advertising, and other optional cookies(/googleCookiePolicyLink) when you watch embedded videos. To watch this video, you need to turn on optional cookies for the site. By clicking “Accept and Play Video,” you will automatically turn on advertising and other optional cookies for the site and accept our (nvidiaTermsOfServiceLink)Terms of Service(/nvidiaTermsOfServiceLink) (which contains important waivers). Please see our (nvidiaPrivacyPolicyLink)Privacy Policy(/nvidiaPrivacyPolicyLink) and (nvidiaCookiePolicyLink)Cookie Policy(/nvidiaCookiePolicyLink) for more information.

Cancel

Accept and Play Video

Alternatively, you can (youtubeLink)watch this video on YouTube(/youtubeLink).

### Climate Simulation and Energy Efficiency

Digital twins are even being applied to climate modeling and energy efficiency initiatives.

NVIDIA’s [Earth-2 is a climate digital twin cloud platform](https://nvidianews.nvidia.com/news/nvidia-announces-earth-climate-digital-twin) designed to enhance the simulation and visualization of weather and climate on a global scale. This platform is part of NVIDIA’s broader initiative to address the economic and safety impacts of extreme weather conditions, which are exacerbated by climate change.

By utilizing AI surrogates, Earth-2 enables the creation of interactive, high-resolution simulations that range from global atmospheric conditions to local weather events like typhoons and turbulence. Earth-2 enables faster and more accurate weather forecasting, which is critical for timely disaster response and planning.

Digital twins are significantly [enhancing energy efficiency across various industries](https://blogs.nvidia.com/blog/digital-twins-modulus-wistron/) by enabling more precise and faster simulations and operations.

For instance, [Wistron](https://www.wistron.com/en) has utilized [NVIDIA PhysicsNeMo](https://developer.nvidia.com/modulus)™ and Omniverse libraries to create digital twins that simulate airflow and temperature in testing facilities. This has reduced simulation times from hours to seconds, improving [energy efficiency](https://www.nvidia.com/en-us/glossary/energy-efficiency.md) by up to 10% and reducing carbon emissions. Similarly, [Siemens Energy](https://www.siemens-energy.com/global/en/home.html) is accelerating simulations of heat-recovery steam generators, reducing potential downtime and fostering greater [sustainable computing](https://blogs.nvidia.com/blog/what-is-green-computing/) practices.

## Next Steps

### Discover Digital Twin Use Cases

Explore industry use cases and how to develop physics-based, AI-enabled digital twins with OpenUSD.

[Explore Digital Twin Use Cases](https://www.nvidia.com/en-us/use-cases/?page=1&workloads=Simulation%20%2F%20Modeling%20%2F%20Design)

### Explore Key Insights From Five Industry Leaders

Dive into real-world developer use cases and access insights and resources to develop your own digital twin solutions.

[Read a Free Ebook](https://resources.nvidia.com/en-us-omniverse-industrial-digital-twins/omniverse-enterprise-5-steps?lx=deNrXD)

### Build Digital Twins With Omniverse Libraries

Integrate NVIDIA Omniverse libraries to develop industrial digital twins and robotics simulation applications.

[Review Developer Resources](https://developer.nvidia.com/omniverse)

[Explore Learning Path](https://www.nvidia.com/en-us/learn/learning-path/digital-twins.md)