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[H1]
What is Physical AI?

Sumit Vishwakarma,

Todd Koelling,

Rich Goldman

Apr 23, 2026
/
11 min read

Table of Contents

Definition

How Physical AI Works

Applications of Physical AI Across Industries

Physical AI Challenges

Simulation's Role in Enabling Physical AI

[H2] Definition

[H2]

Physical AI refers to applications in which digital artificial intelligence (AI) tools are connected to hardware that senses and executes actions in the physical world. This integration enables machines to autonomously act and adapt to real-world situations in real-time. In the past, machinery either carried out a predefined set of actions or selected actions from a set of possibilities using logical decision-making. AI-powered physical systems use a variety of artificial intelligence algorithms to interpret data and infer actions beyond simple if-then-else algorithms.

[H2]

Recent advances in generative AI, improvements to machine learning, and practical solutions for edge computing enable the growing application of physical AI. AI-enabled physical systems are finding their way into an increasing list of applications, removing the need for a human in the loop to make decisions based on changing data.
As these systems evolve toward safety-critical applications in industries such as automotive, aerospace, and healthcare, ensuring their reliability and predictability becomes essential. This introduces the need for robust validation and verification approaches alongside AI model development.

[H2]
How Physical AI Works

Autonomous systems employ physical AI in many applications by gathering, interpreting, acting on, and learning from information in addition to leveraging advanced AI technology and the latest hardware for sensing, computing, and physical action.
To support this lifecycle, development increasingly relies on scalable simulation environments that allow engineers to test system behavior across a wide range of conditions before real-world deployment.
AI Models
The foundational step of any physical AI application is building and training its AI models. To do this, engineers first identify which AI technologies are best for predicting the behavior of the physical system the team is automating. This includes large language models (LLMs), small language models, and more traditional AI tools such as machine learning (ML).
Foundation models are the most common type of model used in physical AI. They are called world foundation models (WFMs) because they are trained on physical data. Because these neural networks are trained on large datasets, they can handle a wide range of use cases.
Hardware
Once model training is done, the next step is to assemble the hardware needed for the physical AI application. This hardware can be broken into four categories: training environment, sensors, computing resources, and actuators and output.
Training Environment
Teams responsible for training AI models use high-performance computing (HPC) data centers to build and train them. A great example of this is NVIDIA Omniverse, which is a collection of libraries and microservices for developing physical AI that runs on GPU-enabled hardware on AWS, Azure, or self-hosted computer infrastructures.
These environments often incorporate digital twin technologies to create virtual representations of real-world systems, enabling more controlled, repeatable, and scalable experimentation.
Sensors
The input connection between the physical and digital worlds is the set of sensors that provide AI agents with information. Engineers use the term multimodal sensing to refer to the acquisition of high-fidelity data from multiple sensors. The most common types of sensors are:

Type of Sensing
Object
Environmental
Internal
Location
Usage
●  Determine if objects exist, size, location, and motion
●  Provide enough sensor data to identify objects by labels or inference
●  Measure physical properties of the environment

●  Measure physical properties of the physical AI hardware

●  Measure location of the physical AI hardware
Examples
●  Video cameras
●  Infrared cameras
●  Still cameras
●  Lidar
●  Radar
●  Sonar
●  Ultrasonic sensors
●  Microphones
●  Temperature sensors
●  Humidity sensors
●  Gas monitors
●  Pressure sensors
●  Flow sensors
●  Accelerometers
●  Gyroscopes
●  Force and torque sensors
●  Encoders
●  Tactile sensors

●  GPS
●  Proximity sensors
●  Real-time location system (RTLS)

[H2]

Computing Resources
Physical AI systems use a combination of remote, local, and edge computing resources for calculations that interpret sensor data and make decisions. Edge computing, in addition to leveraging advances in thermal management, multi-chip packaging, and power management, can be deployed alongside the rest of the hardware to access AI models in real time.
Actuators and Output
The interaction of a physical AI system with the real world occurs through actuators that convert commands into motion and output devices that provide information to humans and other digital and physical systems. Common examples of actuators and output devices are:

[H2]

●       Motors
●       Linear actuators
●       End effectors
●       Hydraulic pistons
●       Pneumatic pistons

[H2]

●       Pumps
●       Solenoids
●       Valves
●       Voice coils
●       Piezoelectric actuators

[H2]

●       Displays
●       Speakers
●       Safety lights
●       Bluetooth, Wi-Fi, and other protocols

[H2]

Process
The AI models and hardware are combined in a physical AI system to carry out its assigned tasks. The industry breaks down the process into four steps:

[IMG: physical ai 4 step infographic]

[H2]

1. Perceive
The first step is to gather data about the physical world around the system. Various sensors in or near the system produce datasets that the physical AI system breaks down into information useful for understanding and making decisions about the physical environment.
2. Reason
The AI tools mentioned above then come into play in the second step. The datasets are consumed by the AI workflow to interpret what is happening and then decide how to react. This goes beyond the if-then-else logic of traditional machine control systems, enabling real-time decision-making.
3. Act
In the third step, the AI workflow takes the advice generated in the reasoning step and produces commands for the actuators. Actions can also be sent to output devices, such as speakers and displays.
4. Learn
The fourth step takes the results of the act step and incorporates them into the AI models to improve the system's performance. This step is optional and can be done remotely at the data center or lab. The learning step can also be done locally in the device, in which case it becomes “embodied AI.”
A good way to better understand how AI-driven applications apply these steps is to look at a simple example, such as a robot arm that takes six different types of donuts from a tray and places one of each type in a box. In this scenario:
The prototype system is trained in a lab or using synthetic data from simulation on as many scenarios as possible. (Training)
In operation, a camera (Sensor) over the tray sends data (Perceive) to the local edge AI systems (Computing Resource), where the AI software stack uses computer vision algorithms to identify and classify the donuts (Reason).
The AI software stack then tells the robot arm (Act) to use its end effector (Actuator) to pick up a donut and put it into an empty box using a camera over the boxes (Perceive, Sensor).
This is repeated until the system uses the image from the box camera (Perceive) to decide the box is full (Reason), and sends a signal to the conveyor belt (Act) under the box to move to the next box, and for a light to flash and a buzzer to buzz (Output) to tell the donut shop workers to take the box.
If a donut clips the edge of a box (Perceive), then that information is used to train the system remotely at the data center or lab or locally in the device (embodied AI) to avoid that situation (Learn).
In practice, validating each of these steps across a wide range of real-world scenarios is critical, as physical AI systems must operate reliably under varying conditions that cannot be exhaustively tested physically.

[H2]
Applications of Physical AI Across Industries

Breakthroughs in sensor technology, GPU-driven high-performance computing, digital twin environments, and artificial intelligence tools have dramatically expanded the variety of applications for physical AI. These advances also enable earlier testing and validation of system behavior through simulation before deployment in real-world environments. Until recently, it was primarily used with computer vision for industrial robots and leading-edge self-driving cars. It is now finding its way into real-world applications across the aerospace, energy, healthcare, and consumer products industries.
Here are a few of the more exciting applications:
Automotive: Automated Driving Systems (ADS) and Advanced Driver-Assistance Systems (ADAS) - Many modern vehicles include ADAS to manage the distance between vehicles and steer the car using lane markings. Autonomous vehicles are also becoming more common in some cities. This is the form of physical AI people interact with the most.
Health Care: Robotic Surgery to Patient Monitoring - Surgeons' skills are being supplemented by robotic surgery systems that adapt to real-time data during a procedure. Another strong healthcare example is how patient care systems are linking wearable sensors to smart speakers and cameras to enable autonomous patient monitoring, including the ability to trigger physical actions.
Manufacturing: Industrial Robots - The manufacturing industry is rapidly transforming production lines into more flexible and efficient manufacturing systems using physical AI.
Aerospace: Unmanned Aerial Vehicles (UAVs) - In the past, UAVs were remotely piloted or used basic programming. With physical AI, UAVs can now navigate using simple instructions, avoid obstacles, adapt to complex environments, and execute more complex missions.
Multiple Industries: Autonomous Mobile Robots (AMRs) - Humans and older, pre-programmed robots are being replaced by purpose-built mobile robots that can locate, retrieve, and move objects. Amazon has been a leader in applying physical AI for AMRs in its fulfillment centers. These systems are also making their way into healthcare facilities to deliver food and supplies, and are even appearing in restaurants to deliver food. Autonomous floor cleaners, or cleaning bots, are another good example of how people are using AMRs for industrial, commercial, and home cleaning. These systems have evolved beyond using computer vision and proximity sensors for navigation. They can now identify objects and determine the cleaning required.
Multiple Industries: Humanoid Robots - Robots that look like and mimic the actions of humans are one of the most visible forms of physical AI in the media. Currently, humanoid robots are used for picking and material handling in automotive manufacturing and conducting tasks previously done by AMRs in warehouses.

[H2]
Physical AI Challenges

Even though physical AI has moved from R&D into real-world applications, the tech

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[H1] Electronics Digital Twins
Electronics Digital Twins

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[H2]
What is an Electronics Digital Twin (eDT)?

A digital twin is a virtual representation of a physical product, system, or process synchronized with the in-service physical asset. Digital twin technologies enable advancement in design, lifecycle management and predictive maintenance of the physical asset.
With the drive to AI-enabled, software-defined products, there is a need to extend the digital twin paradigm. An Electronics Digital Twin (eDT) is a virtual representation of an electronic system. eDTs enable engineers to “shift left” and accelerate design, verification, and software development throughout the product lifecycle.

[IMG: What is Electronics Digital Twin]

[IMG: Synopsys eT Solutions]

[H2]
Synopsys eDT Platform

eDT Platform is an open solution to accelerate the creation, management, deployment, and use of eDTs critical for today’s software-defined product development for physical AI.
The platform includes:
Synopsys’ leading virtualization and AI technologies
System composition using the open-source SIL Kit by Vector and Synopsys
A broad set of pre-integrated ecosystem partner technologies
Comprehensive user management, analytics, and workflow creation
User interfaces, applications, and APIs to integrate with customer infrastructure
Flexible compute options in cloud-based environments

[H2]
Using eDT Platform for the Automotive Industry

The platform is well-suited for the transformation of automotive development towards AI-enabled software-defined vehicles (SDVs), which requires increasing collaboration across the automotive ecosystem.
Example use cases include:
Early design evaluation
Early software development
Collaborative software development
Efficient system validation

[IMG: Automotive Solutions with eDT]

[H2]
Key Benefits

[IMG: Reduce Development Time & Cost]

Reduce Development Time & Cost
Achieve faster time to market by achieving milestones reliably and efficiently.

[IMG: Increasing Product Quality]

Increasing Product Quality
Reduce maintenance costs and recalls due to more efficient and continuous validation of over-the-air (OTA) updates.

[IMG: Accelerate Innovation & Differentiation]

Accelerate Innovation & Differentiation
Bring new features to market faster and continuously.

[IMG: Increased User & Customer Satisfaction]

Increasing User & Customer Satisfaction
Accelerate deployment and growth with simplified administration and availably of eDT Labs.

[H2]
Related Products

Virtual Prototyping Solutions

Hardware-assisted Verification Solutions

SIL Kit

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What's New

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[H4]
Synopsys Accelerates Physical AI System Development with New Electronics Digital Twin Platform

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Unveiling the Synopsys Electronics Digital Twin (eDT) Platform

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Software Development, System Validation, and Collaboration with Electronics Digital Twin

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[H2]
Multiphysics Fusion Technology for Multi-Die Designs Explained

[H3]
Unified multiphysics fusion helps multi-die teams validate earlier and sign off faster.

[IMG: Automotive Executive Guide: Rethinking Automotive Development]

[H2]
Automotive Executive Guide: Rethinking Automotive Development

[H3]
A guide to virtualization in software-defined vehicles for automotive leaders.

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Mastering AI Chip Complexity

[H3]
This eBook explores AI chip design trends, challenges,
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Advancing the Transformation from Overdesign to Co-Design
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[H2]
Bringing Physics-Aware Co-Design Across the Chip Design Flow

[H2]

As semiconductor designs push into advanced nodes, higher power densities, and complex multi-die designs, traditional single domain design approaches are no longer sufficient. Multiphysics Fusion™ integrates silicon design automation with high fidelity multiphysics analysis—spanning electrical, thermal, electromagnetic, and mechanical effects—to help engineering teams design with confidence at leading-edge complexity.
By fusing trusted EDA workflows with gold standard multiphysics analysis, Multiphysics Fusion enables earlier insight, higher-accuracy signoff, better reliability, and faster design convergence—resulting in better power, performance, and area (PPA) outcomes and reduced risk across the entire design flow.

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[H2]
Accurate Timing Under Real Operating Conditions

Timing signoff at advanced nodes must account for physical effects that directly influence transistor behavior. Voltage drop (IR drop), temperature variations, thermal mechanical and assembly stress and significantly impact timing, especially in high performance computing (HPC) and AI designs operating at extreme conditions.
Multiphysics Timing Signoff brings IR-aware, thermal-aware and stress-aware analyses directly into timing signoff, enabling engineers to:
Evaluate timing with realistic power and temperature conditions
Accurately model the delay impacts due to IR-drop and thermo-mechanical stress
Reduce over conservative margins without sacrificing reliability
Improve correlation between in design analysis and final signoff
The result is more robust timing closure, faster signoff turnaround, and optimized silicon performance.

Explore PrimeTime Multiphysics Timing Signoff

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Signoff-accurate Multiphysics Closure with PPA-optimized Convergence

At advanced nodes, dynamic voltage drop (IR drop), stress, and thermal effects become a leading limiter of timing, performance, and reliability. Rising resistance, higher frequencies, and escalating power density amplify IR and thermal effects - often surfacing late in the flow and driving costly ECO iterations.
Multiphysics Fusion integrates signoff-accurate voltage awareness directly into the design closure loop.  By leveraging RedHawk-SC Sigma insights, PrimeClosure enables surgical, timing-safe fixes that proactively resolve IR and thermal issues—eliminating the inefficiencies of traditional analysis-ECO-P&R cycles.
Key benefits and Proven Impact:
Signoff-accurate, in-design multiphysics closure (IR + thermal + timing)
Up to 85% fewer IR violations with timing safe, PPA-optimized fixes
Up to 20x faster IR closure, saving multiple weeks in schedule
By integrating multiphysics effects throughout the closure flow, teams achieve faster convergence, improved PPA, higher reliability, and more predictable tapeout outcomes.

Explore Synopsys PrimeClosure

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Unified Analysis for 2D, 2.5D, and 3DIC Packaging

Multi die design is foundational to modern HPC and AI systems, but it introduces new engineering challenges that span power, thermal, signal, electromagnetic and structural domains, and the interactions among them. Traditional chip centric tools cannot fully capture these cross-domain effects that blur the lines between chip and system and increasingly determine system performance, reliability, and manufacturability.
Multiphysics Fusion for multi-die design brings together multiphysics analysis and the full EDA stack in a unified exploration-to-signoff platform, enabling comprehensive analysis across the design lifecycle, including:
Thermal simulation and optimization across dies, interposers, and board-level packaging
Hierarchical power integrity signoff for entire multi-die designs, including microbumps, TSVs, and packaging effects
Efficient routing and signal integrity optimization for high-speed die-to-die interconnects, such as UCIe and HBM, informed by early parasitic prediction and trusted electromagnetic-aware analysis
Consistent multiphysics analysis from prototyping through floorplanning and foundry-certified signoff
Simulation of mechanical stress and warpage to evaluate lifecycle and reliability risks
By unifying multiphysics insight across dies, interposers, and packaging, engineering teams can reduce integration risk, accelerate schedules, and deliver more robust multi-die products at scale.

Explore Synopsys 3DIC Compiler platform

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Multiphysics Accurate Analysis for RF and Analog and Mixed Signal Innovation

As modern chips integrate advanced high-speed interface PHYs to meet rising chip-to-chip data-rate and bandwidth demands—and as next-generation Wi-Fi and mobile standards continue to grow in complexity—analog, mixed-signal, and RF engineers need electromagnetic analysis that is accurate, trusted, and easy to use. To improve engineering productivity, multiphysics analysis must be seamlessly integrated into the design environment, together with highly accurate parasitic extraction, to support advanced RF simulation and signal and power integrity analysis from the earliest design stages through signoff.
Multiphysics Fusion brings advanced analog and electromagnetic analysis into a modern analog design environment, enabling engineers to:
Simplify electromagnetic analysis and RF simulations by integrating multiphysics HFSS-IC solvers with the Synopsys Custom Compiler layout environment
Perform intelligent passive components synthesis, producing editable PyCell devices in Custom Compiler layout and schematic environments
Accurately capture on-chip high-frequency parasitics and layout-dependent effects that shape analog and mixed-signal behavior
Detect electromagnetic-induced signal integrity, performance, and reliability issues earlier in the design cycle
Achieve stronger correlation to final signoff for higher-confidence design decisions
By integrating highly accurate and high-capacity electromagnetic analysis solvers into the design and signoff flow, teams can push performance boundaries in analog, mixed‑signal, RF and high‑speed designs without sacrificing accuracy or schedule predictability.

Discover Synopsys Custom Compiler

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Experience Synopsys VeloceRF Synthesis Tool

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Get Started with Ansys HFSS-IC

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Resources

News Release

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Synopsys Announces Availability of the First Wave of Multiphysics Fusion Solutions

Learn more

eBook

[H4]
Multiphysics Fusion Technology for Multi-Die Designs Explained

Download

Blog

[H4]
From Overdesign to Co-Design: Confronting Multiphysics Challenges in Chips

Read

Blog

[H4]
Breaking the Design Closure Bottleneck with Timing-Aware Multiphysics ECO

Read

Blog

[H4]
New Synopsys Multiphysics Fusion™ Technology Set to Transform Chip and Product Engineering

Read

“Synopsys is using NVIDIA accelerated computing and CUDA-X libraries, including cuDSS, which delivers up to 13x GPU acceleration, to scale increasingly complex SPICE simulations, electromagnetics, and power-integrity workloads. In addition, Synopsys Multiphysics Fusion solutions enable up to 5x faster design closure ...”

Tim Costa

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VP & GM of Computational Engineering at NVIDIA

[IMG: NVIDIA]

'By unifying multiphysics analysis and timing signoff across digital, analog, photonic and multi-die designs, Synopsys Multiphysics Fusion technology gives us earlier insight into cross-domain interactions across silicon, advanced packaging and optical domains, which makes it possible for us to improve predictability, reduce late-stage rework, and achieve a runtime that’s 10 times faster than before.”

Harrison Hsieh

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Vice President at MediaTek

[IMG: MediaTek]

"Accurate timing signoff at advanced nodes requires a unified approach that accounts for IR drop, thermal, and stress effects directly within timing analysis.” “Synopsys’ Multiphysics Fusion technology provides a unified, all‑aware timing signoff platform by integrating PrimeTime with multiphysics insight, delivering SPICE-accurate correlation and enabling margin recovery. This is increasingly important as we pursue higher levels of integration, performance, and reliability across advanced process and multi‑die technologies.”

Hyung-Ock Kim

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Vice President and Head of the Foundry Design Technology Team at Samsung Electronics

[IMG: Samsung Logo]

"The Cisco Silicon One group is leveraging Synopsys Multiphysics Fusion technology to unify IR drop effects within signoff design closure to gain earlier, more accurate visibility into real-world conditions. Combined with signoff-accurate, timing-aware IR fixing, this enables predictive optimization—helping Cisco Silicon One converge on power integrity issues faster, deliver better PPA, and achieve significantly faster runtime."

Silicon One Group

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Cisco

[IMG: Cisco]

“As SoC designs demand optimal user experience and battery efficiency, achieving fast and predictable design closure under real-world operating conditions is critical. By leveraging Synopsys Multiphysics Fusion technology to integrate IR drop for signoff design closure with timing-aware analysis, we can identify and resolve power integrity issues earlier across complex, power-constrained designs. This enables more efficient convergence during design closure and improves PPA for our complex workloads.”

Ouyang

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Vice President at Sanechips

[IMG: Sanechips]

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FAQ

[H4] What is Multiphysics Fusion Technology and why is it needed?

Multiphysics Fusion Technology integrates silicon design automation with high-fidelity multiphysics analysis, spanning electrical, thermal, electromagnetic, and mechanical effects, within a unified workflow. As designs push into advanced nodes with higher power densities and complex multi-die architectures, traditional single-domain approaches can no longer capture the cross-domain interactions that impact performance, reliability, and manufacturability. Multiphysics Fusion enables earlier insight, higher-accuracy signoff, and faster design convergence across the entire design flow.

[H4] How does Multiphysics Fusion address timing signoff at advanced nodes?

At advanced nodes, physical effects like IR drop, temperature variation, and thermo-mechanical stress directly impact tr