How virtual twins, accelerated computing and physics-based models are turning AI from smart software into a reliable engineering infrastructure
For more than a decade, artificial intelligence has transformed how organizations analyze data, identify patterns and make decisions. It has streamlined processes, accelerated response times and become an integral part of modern information systems.
But as AI moves deeper into the core of industry, into production lines, engineering systems, materials and critical infrastructure, a gap is becoming clear. The tools that worked in the digital world are not sufficient in the physical one.
In industrial environments, decisions are not based on probability alone. They are governed by the laws of physics, material behavior, design constraints and strict regulation. It is no longer enough for AI to predict an outcome. It must also explain why it happens, define its validity limits and show how changes in one parameter affect an entire system.
This is where the next generation of AI is emerging: Physical AI.
Physical AI anchors artificial intelligence in physics-based models, engineering simulations and scientifically validated virtual twins. It marks a shift from AI as a decision-support tool to AI as an engineering-grade foundation that can be trusted in mission-critical systems.
The recently announced collaboration between Dassault Systèmes and NVIDIA highlights this transition. This is not a point solution or a software integration. It is an attempt to build a broader industrial architecture that combines virtual twins, accelerated computing and accumulated engineering knowledge as the basis for scalable, reliable AI.
Beyond Statistics: Physics as the Basis for Trust
In consumer applications, AI can tolerate a degree of inaccuracy. A weak recommendation or a vague answer is rarely critical.
In industry, every decision affects safety, system availability, cost and compliance. Industrial AI cannot rely solely on statistical learning from historical data.
Engineers and system designers must understand how a decision was made, what assumptions it relies on and where its limits lie. This is where the virtual twin becomes essential, not as a graphical model, but as a living engineering representation of a real system.
When AI operates on a validated virtual twin, it derives insights from physical laws, material data and known processes. This is what enables AI to be deployed in mission-critical environments where reliability is not optional.
Virtual Twins: From Design Tool to Operational Layer
The concept of the virtual twin is not new, but its role is changing.
Traditionally, it served as a simulation tool used during the design phase. Today, it is evolving into a central layer of truth that supports design, simulation and decision-making across the lifecycle.
Modern virtual twins are continuously updated. Changes in geometry, materials, load conditions or manufacturing processes are immediately reflected in the model’s behavior.
This allows engineers to test scenarios, identify failure points and evaluate impacts before they occur in reality.
The automotive industry provides a clear example. Instead of conducting thousands of physical crash tests for a new vehicle, a process that is both expensive and time-consuming, Physical AI enables hundreds of thousands or even millions of simulations.
These are not statistical approximations. They are physics-based models that reflect how steel, aluminum and composite materials behave under real conditions. The result is shorter development cycles, fewer costly physical tests and earlier, model-driven design decisions.
At the same time, the virtual twin creates a shared language between design, engineering and manufacturing teams, reducing misalignment and interpretation gaps.
Engineering Agents: From Interaction to Collaboration
If the virtual twin is the source of truth, AI agents are the interface to it.
These are not generic chatbots or help desk tools. Industrial agents operate within a defined engineering context.
They understand specific systems such as production lines, products or processes, and rely on virtual twins and validated physical models.
When an engineer modifies a design, the agent can analyze the implications, flag deviations and suggest alternatives, while clearly explaining the engineering logic behind its recommendations.
This fundamentally changes the interaction model. Instead of navigating dozens of dashboards and reports, engineers can engage in focused dialogue with the system and receive insights grounded in physics.
AI does not replace human expertise. It amplifies it, allowing engineers to focus on higher-value decisions.
Accelerated Computing: Scaling Physical AI
For virtual twins to operate in real time and for AI agents to deliver reliable insights, significant computational power is required.
This is where accelerated computing becomes critical.
Physical AI combines GPUs, advanced computing libraries and physics-based AI models, enabling complex simulations to run as part of everyday workflows.
In the past, engineering simulations were performed at discrete stages in development. Accelerated computing changes that model. Simulation becomes continuous and integrated into the design process itself.
This enables adaptive design, faster evaluation of alternatives and the ability to deploy consistent models across multiple sites and factories.
Sovereign AI and Data Control
For industrial AI to be viable, it must ensure full data sovereignty.
In sectors such as defense, healthcare and aerospace, the main concern is not whether to use AI, but how to prevent sensitive data and intellectual property from leaking into uncontrolled environments.
In this context, the Dassault Systèmes and NVIDIA partnership emphasizes a Sovereign AI approach.
Through its OUTSCALE cloud, Dassault Systèmes is deploying AI Factories across multiple regions, enabling organizations to run large-scale Physical AI models while maintaining strict control over data privacy, intellectual property and digital sovereignty.
This approach allows companies to adopt advanced AI without compromising control over their most critical assets.
Model-Based Systems Engineering: A Two-Way Synergy
Another key element of the collaboration is Model-Based Systems Engineering, MBSE.
This approach enables complex systems to be designed, analyzed and validated using digital models throughout their entire lifecycle, not just during initial design.
The synergy is bidirectional.
Dassault Systèmes integrates NVIDIA’s AI infrastructure into its virtual twin platforms, while NVIDIA itself leverages Dassault’s engineering models and MBSE tools to design next-generation computing platforms and AI infrastructure.
Together, MBSE, virtual twins and Physical AI create a continuous engineering workflow, from requirements definition to simulation, validation and large-scale deployment.
Beyond Machines: Expanding into Biology and Materials
Physical AI is not limited to mechanical systems or manufacturing environments.
The same principles apply to biology and materials science.
Instead of relying on lengthy trial-and-error experimentation, researchers can create virtual twins of molecules, proteins and advanced materials, and simulate their behavior within environments governed by physics and chemistry.
By combining accelerated computing with scientific models, Physical AI can accelerate drug discovery, enable the development of more efficient materials and significantly reduce development time and cost.
The Challenges: Data and Talent
Despite its promise, Physical AI introduces real challenges.
The first is data quality. Industrial AI depends on reliable field data collected from production systems, sensors and equipment. Incomplete, unstructured or poorly synchronized data can limit the accuracy of physical models.
The second challenge is talent. Physical AI requires a combination of deep engineering and physics expertise alongside data science and AI capabilities.
For many organizations, the bottleneck is not the technology itself, but the ability to bridge these domains and build teams that share a common technical language.
A Strategic Shift, Not a Shortcut
For industrial leaders, the key question is no longer whether to adopt AI, but how to build it correctly.
The first step is not acquiring another AI tool, but defining an engineering data strategy, mapping existing physical models, assessing data quality and ensuring consistency between them.
At the same time, organizations must invest in human capital, teams that understand both physical systems and data-driven models.
Physical AI does not promise shortcuts. It offers something more valuable: a more precise, scalable and reliable way to engineer intelligence as part of the physical world.
Credits: Based on official publications by Dassault Systèmes and NVIDIA
New-Tech Magazine Group
