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Accelerate Physical AI Development with Trusted Simulation
Physical AI embeds intelligence into machines that sense, interpret, decide, and act in the physical world—from autonomous vehicles and industrial robots to aerospace systems and medical devices. Unlike traditional automation, these systems must adapt to dynamic real-world conditions, making reliability, predictability, and validation essential. Synopsys delivers a simulation-first path for Physical AI, combining 50 years of Ansys physics-based engineering simulation expertise with 40 years of Synopsys silicon-to-systems leadership to help teams train, test, and validate intelligent systems before deployment.
What's New
Accelerating Autonomous Robotics with Ansys Sensor Simulation
Discover how simulation aids in developing robust optical and RF sensor solutions, enhancing safety and efficiency before physical testing.
Ansys Autonomous Vehicle Simulation (AVxcelerate)
Discover how AVxcelerate 26R1 combines Omniverse digital twins, physically accurate camera simulation, and advanced radar tooling.
Key Benefits
Capabilities
High-Fidelity Multiphysics Modeling for AI Training
Physical AI systems require diverse, accurate training data that reflects real-world physics. Synopsys multiphysics simulation spans electromagnetics, optics, fluids, structures, and thermal behavior, enabling teams to generate synthetic datasets that capture sensor performance, actuator dynamics, and environmental interactions across a wide range of operating conditions.
Ansys tools—including Ansys AVxcelerate ®, Ansys Maxwell®, Ansys SPEOS®, and Ansys HFSS™ for radar modeling—integrate with platforms like NVIDIA Omniverse to create parametric, physics-based models. These models feed world foundation models (WFMs) with high-fidelity data, reducing the cost and time associated with physical data collection while expanding scenario coverage.
Engineers use Ansys ModelCenter®, Ansys Twin Builder™, and Ansys optiSLang® to create reduced-order models that scale across training environments. The result: faster model convergence, improved generalization, and reduced Sim-to-Real gaps in deployed systems.
Ansys AI further accelerates this simulation-led workflow. Ansys SimAI™ helps teams predict new design behavior in minutes by learning from trusted simulation data, making it easier to explore more design alternatives without running every scenario through traditional solvers. Ansys GeomAI™ complements this approach by learning from existing geometries to generate and explore new design concepts, helping engineers expand the design space while preserving design intent.
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Physics-Grounded Digital Twins for Continuous Validation
Digital twins are evolving from static visualizations to connected learning systems that predict, optimize, and act. Synopsys enables this evolution by combining high-fidelity multiphysics simulation, reduced-order modeling, and AI-driven analytics into simulation-based digital twin connected to the physical assets for live maintenance operations and support continuous improvement.
Ansys Twin Builder™ integrates component-level physics models—created with Ansys Maxwell®, Ansys Fluent®, Ansys Icepak®, and other solvers—into system-level digital twins. TwinAI applies AI and machine learning to simulation data, enabling predictive maintenance, anomaly detection, and performance optimization. Engineers deploy these twins in industrial environments connected to the physical assets to monitor asset health, simulate what-if scenarios, and optimize throughput in real time.
The strategic partnership with NVIDIA extends digital twin capabilities into immersive, scalable environments. Synopsys simulation assets export to Omniverse using Universal Scene Description (USD), enabling teams to visualize, test, and validate Physical AI systems across virtual and physical boundaries. This approach reduces downtime, accelerates certification, and improves mission readiness for aerospace, defense, energy, and manufacturing customers.
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Silicon-to-Systems Design for Edge AI Hardware
Physical AI systems depend on energy-efficient, thermally optimized edge computing hardware. Synopsys delivers a complete EDA and multiphysics simulation flow that spans chip architecture, power integrity, thermal management, and system-level reliability.
Synopsys Platform Architect™ explores power optimization at the architectural level, while Synopsys ZeBu® EmPower and Synopsys SpyGlass® Power evaluate RTL-level power consumption. Synopsys RedHawk-SC™ provides digital power integrity sign-off for multichip packages used in modern CPUs and GPUs. At the system level, Ansys Icepak® simulates electronics cooling, and Ansys Sherlock™ predicts reliability under operational stress.
This integrated approach enables teams to design edge AI hardware that meets the latency, power, and thermal requirements of autonomous systems. Engineers validate chip-to-board-to-enclosure performance before fabrication, reducing prototype cycles and ensuring that deployed systems operate reliably in dynamic real-world environments.
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Industries
End-to-End Robotics Simulation from Design to Deployment
Industrial robotics systems require precise coordination of sensors, actuators, embedded software, and control algorithms. Synopsys delivers a complete simulation workflow that spans structural optimization, electromechanical design, sensor validation, and digital twin integration, enabling teams to design, test, and deploy intelligent robots faster.
Ansys Mechanical™ and Ansys Motion™ simulate structural response and multibody dynamics, while Ansys Motor-CAD® and Ansys Maxwell® optimize electric motor performance for precision and efficiency. Ansys Zemax OpticStudio® and Ansys Lumerical INTERCONNECT™ model optical sensors, and Ansys SCADE Suite® automates safety-certified embedded software generation for control systems.
Engineers integrate high-fidelity component models into system-level digital twins using Ansys Twin Builder™, then validate performance across operational scenarios. This approach reduces reliance on physical prototypes, pinpoints thermal, power, and safety issues earlier, and shortens development cycles. Customers in manufacturing, logistics, and healthcare use these workflows to deploy autonomous mobile robots (AMRs), humanoid robots, and collaborative systems with confidence.
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AI-Enabled in Silico Modeling for Cardiovascular Systems
Medical device developers, researchers, and clinicians need high-fidelity simulation to visualize cardiac anatomy, test device designs, and plan interventions. Synopsys provides a reference framework combining Ansys LS-DYNA®, NVIDIA NIM AI-enabled chatbot interfaces, and Omniverse visualization to enable immersive cardiovascular modeling.
Users prompt the chatbot with natural language queries, and customized PyAnsys-Heart code is automatically generated, enabling high-fidelity simulation without requiring deep simulation expertise. The framework supports patient-specific anatomies, pathology visualization, and medical device testing in realistic physiological environments.
This workflow integrates with NVIDIA Isaac for Healthcare, enabling developers to prototype AI-enabled robotic systems for interventional procedures, diagnostics, and hospital automation. By moving from 2D imaging tools to 3D physics-based digital twins, clinicians gain insights into treatment outcomes before a patient reaches the operating room, improving decision-making and patient safety.
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Physics-Based Sensor Simulation for Autonomous Vehicles
Autonomous vehicle development depends on accurate sensor simulation to validate perception algorithms across diverse driving conditions. Ansys AVxcelerate Sensors™ delivers GPU-native, physics-based simulation of cameras, radar, lidar, and thermal sensors, enabling teams to test vehicle perception in virtual environments before road testing.
The 2026 R1 release integrates NVIDIA Omniverse, closing the loop between real and virtual worlds. Engineers can digitally reconstruct driving environments, apply Ansys multiphysics materials, and run high-fidelity sensor simulations that replicate complex lighting, weather, and environmental factors. Light propagation engines model true multispectral behavior, while physics-based radar modeling captures signal interactions with materials, surfaces, and angles.
This integration supports controlled variation through Omnigraph and randomization tools, enabling teams to generate diverse validation datasets without manual repositioning. The outcome: scalable, reusable sensor-aware digital twins that accelerate certification, reduce prototype dependency, and improve confidence in real-world performance.
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Resources
2026 R1 AVxcelerate Sensors Software Integrates NVIDIA Omniverse To Speed Self-Driving Innovation
Top Executives Share Their Views on the Future of Digital Engineering in the Age of AI
FAQ
Physical AI embeds intelligence into machines that sense, interpret, decide, and act in the physical world, enabling systems to adapt to dynamic real-world conditions. Unlike traditional automation, which follows predefined logic or simple if-then-else rules, Physical AI uses machine learning, foundation models, and sensor fusion to infer actions in real time. Applications span autonomous vehicles, industrial robots, aerospace systems, and medical devices, where reliability and predictability are essential.
Simulation generates physics-accurate synthetic data for AI model training, reducing reliance on costly physical testing and expanding scenario coverage. Synopsys multiphysics solvers model sensors, actuators, fluids, structures, electromagnetics, and thermal behavior across operational edge cases. Engineers use these models to train world foundation models, validate perception algorithms, and test autonomous systems in virtual environments before real-world deployment, closing the Sim-to-Real gap.
Automotive, aerospace, defense, healthcare, manufacturing, robotics, and energy industries rely on Physical AI to improve safety, efficiency, and autonomy. Autonomous vehicles use physics-based sensor simulation for perception validation. Industrial robots leverage digital twins for performance optimization. Medical device developers use in silico modeling for surgical planning and device testing. Aerospace teams validate UAVs and defense systems in autonomy testbeds before mission deployment.
Synopsys and NVIDIA have a strategic partnership combining trusted physics simulation with scaled compute and immersive virtual environments. Ansys multiphysics tools integrate with NVIDIA Omniverse to create physics-grounded digital twins and generate synthetic data for AI training. AVxcelerate Sensors leverages Omniverse for autonomous vehicle sensor simulation, while LS-DYNA integrates with NVIDIA Isaac for Healthcare to enable AI-enabled robotic system development.
Digital twins provide high-fidelity virtual representations of physical systems, enabling continuous validation across operational scenarios. Synopsys Twin Builder integrates component-level physics models into system-level twins that mirror live operations, predict performance, and optimize behavior. These twins support predictive maintenance, anomaly detection, and what-if analysis, helping teams validate autonomous systems, accelerate certification, and improve mission readiness before physical deployment.