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Has quantum made Nvidia Europe partners in factory revolution even greater opportunities in next 5 yeras than Asian advanced manufacturing corcles of eg tsmg and foxconnDassault Systèmes Unveils a New Way of Working for Industry with ...
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Dassault Systèmesare siemens and dassault 2 of europes super cluster of factory revolution enegineers; oif so any other nvidia partners across europe with as massice engineering interests in factory revolution of digital twin
Yes,
Siemens and Dassault Systèmes are the two undisputed titans of the European "Industrial Metaverse" and factory revolution. They represent the "Super Cluster" of engineering talent because they own the end-to-end software stack—from the molecular level (PLM) to the factory floor (Automation).While they are the leaders, the NVIDIA-Europe partnership ecosystem has expanded significantly to include other massive engineering interests that are equally critical to the factory revolution The European "Big Four" of NVIDIA Factory Partnerships
Beyond Dassault and Siemens, two other European entities have formed "massive" strategic interests with NVIDIA to redefine manufacturing:
| Partner | Specialized Role in Factory Revolution | Key NVIDIA Integration |
|---|---|---|
| Siemens (Germany) | The "Hardware-Software" Bridge. Leads in Industrial Automation (PLM + TIA Portal). | Integrating Omniverse into the Siemens Xcelerator platform for real-time physics. |
| Dassault (France) | The "Scientific Twin." Leads in Materials Science and System-of-Systems simulation. | Using NVIDIA AI Factories to scale 3D generative design and "Sovereign Cloud" simulations. |
| SAP (Germany) | The "Digital Nervous System." Manages the Supply Chain & Logistics data that feeds the twin. | Using NVIDIA AI Foundations to automate supply chain decisions and factory floor logic. |
| ASML (Netherlands) | The "Precision Architect." Engineering the most complex machines ever built (Lithography). | Using NVIDIA cuLitho to accelerate the computational physics needed to design chip-making factories. |
Other Significant European Players
While not as large as Siemens or Dassault, these companies are essential components of the European deep-tech engineering cluster:
- ABB (Switzerland/Sweden): A global leader in robotics. They partner with NVIDIA to integrate Isaac AI into their industrial robots, allowing machines to "learn" how to navigate factory floors autonomously rather than being pre-programmed.
- BMW Group (Germany): While an automaker, BMW is an engineering powerhouse that pioneered the "Factory of the Future." Their Regensburg plant was the first to be completely planned and operated in NVIDIA Omniverse, setting the blueprint for the entire industry.
- Hexagon (Sweden): They specialize in "Reality Capture." They use NVIDIA technology to turn physical factory data (from laser scanners and sensors) into high-fidelity digital twins. If Siemens is the brain, Hexagon is the eyes.
Why this "Super Cluster" is Unique to Europe
The reason these partnerships are so significant is that Europe holds a monopoly on industrial knowledge.
While Asian digital factories (like TSMC or Foxconn) are masters of high-volume execution, Europe (via Siemens, Dassault, and SAP) owns the IP and software architecture used to design those very factories.
- The Asian Model: Primarily "Digital-First" execution and rapid scaling of existing blueprints.
- The European Model: "Science-First" engineering. Europe is focused on Physics-Based AI, where the digital twin doesn't just "look" like the factory—it calculates the stress on every bolt and the airflow of every vent using fundamental laws of physics.
Is Europe "Reinventing Basic Science" more than Asia?
In many ways, yes. While Asia dominates the Advanced Manufacturing Supply Chain (how things are moved and assembled), Europe is doubling down on Computational Science.
Companies like Dassault Systèmes are moving beyond simple 3D models to Multi-Scale Modeling. They are trying to simulate how a chemical molecule behaves in a battery, then how that battery behaves in a car, and then how that car is recycled—all in one digital thread. This requires a level of deep-tech "First Principles" engineering that is the current focus of the European cluste rWould you like me to dive deeper into the specific "Omniverse" projects Siemens is running, or perhaps explore how ASML is using NVIDIA to push the boundaries of physics in chip manufacturing?
Jensen has said that those whose manufacturing software was configured around ceetral processor data may not find it easy to convert - are eg siemens and dassault more flexible than eg sap to leap with facoiry revoltion jensen is building
In this context, Siemens and Dassault are significantly better positioned than SAP to "leap" into the factory revolution. Here is the breakdown of why their "DNA" makes them more flexible for the NVIDIA era
Jensen Huang’s point centers on a fundamental shift in computing architecture: the transition from
Retrieval-based computing (CPUs fetching data from a database) to Generative-based computing (GPUs simulating physics and AI in real-time).1. Geometry vs. Transactions (The "Physics" Advantage)
The "Factory Revolution" Jensen describes is a Physical AI revolution.
- Siemens & Dassault: Their software was built on Geometry and Physics. From day one, they dealt with vectors, fluid dynamics, and stress points. While they used CPUs to calculate these, the math is native to what GPUs do best (parallel processing). Converting a physics engine to run on a GPU is a natural evolution.
- SAP: SAP is a Transactional engine. It deals with tables, rows, and "if-then" logic (e.g., if part X is used, then deduct from inventory). This is "classic" CPU-bound data. SAP doesn't "know" what a factory looks like; it knows what a factory costs and orders. For SAP to leap, it has to completely rethink how it interacts with spatial data.
2. The "Digital Twin" vs. "Digital Record"
Jensen’s vision requires a Virtual Twin that behaves exactly like the real world.
- Dassault & Siemens (The Builders): They create the Digital Twin. Because their software (like Catia or NX) already defines the "Ground Truth" of a product’s shape and material, they can feed that data directly into NVIDIA Omniverse. They are essentially providing the "video game assets" for the industrial world.
- SAP (The Accountants): They create the Digital Record. SAP tells you the serial number of the machine, but it doesn't know the torque of the motor in real-time. For SAP to thrive in the NVIDIA revolution, it must "piggyback" on Siemens or Dassault to give its data a physical home
Jensen’s comment about "configuration" refers to how deeply a company is tied to old ways of processing data.
- SAP’s Challenge: Most SAP implementations are heavily customized, brittle "legacy" systems built over decades. Moving this to a real-time, AI-driven environment is like trying to turn a cruise ship in a canal.
- Dassault/Siemens’ Advantage: They have already spent the last 5–8 years moving toward Cloud-Native and GPU-accelerated kernels. For example, Dassault’s 3DEXPERIENCE platform was designed to break down silos, making it much more "modular" and ready to plug into NVIDIA’s AI clusters
| Feature | Siemens / Dassault | SAP |
|---|---|---|
| Data Type | High-dimensional physics/geometry | Linear tables and logs |
| Hardware Fit | Naturally Parallel (GPU-friendly) | Sequential (CPU-friendly) |
| NVIDIA Synergy | Omniverse: High (Visualizing physics) | NIM/AI: Moderate (Predicting supply chain) |
| The "Leap" | A Turbocharge of existing capability | A Fundamental Re-architecture |
The "European Reinvention" of Basic Science
This is where your point about Europe reinventing basic science becomes vital. Siemens and Dassault are focusing on Synthetic Data.
Because Europe has high labor costs and strict regulations, they cannot "trial and error" in the physical world as easily as some Asian manufacturing hubs. Therefore, they are using NVIDIA to "reinvent" the science of the factory floor virtually first. They are using GPUs to simulate the chemistry of the paint or the molecular stress of a 3D-printed part before a single atom is moved in reality.
3. The "Legacy Debt" ChallengeComparison: Who Leaps Further?Key Insight: In Jensen's world, "software-defined" means the factory is a giant robot. Siemens and Dassault are building the motor skills and brain for that robot; SAP is merely writing the schedule for it.Would you like me to look into how specific "Physics-ML" (Physics-Informed Machine Learning) is being integrated into Dassault's 3DEXPERIENCE to see how they are bypassing old CPU limitations?
so lets take foxconn and tsmg - they are advanced manufacturing chip revolutions but are we saying they miss some of the basic science revolutions of eg einstein maths as chip design may have needed eg critical mineral supply chains but doesnt go eg into all the quantum maths of physical ai
When Jensen Huang talks about Physical AI, he is talking about AI that understands the laws of the universe. This is where Europe’s obsession with "Basic Science" provides a massive advantage in the next leap
This is a profound distinction that cuts to the heart of the current global tech divide. To answer simply:
Yes. While Foxconn and TSMC are the undisputed kings of process engineering and supply chain execution, they operate primarily at the Macro and Nano scales, whereas the European "Super Cluster" (Dassault, Siemens, ASML) is increasingly diving into the Atomic and Quantum scales of "First Principles" science.Here is how to think about the difference between the Execution Revolution (Asia) and the Basic Science Reinvention (Europe)1. TSMC/Foxconn: The Masters of "Iterative Perfection"
TSMC and Foxconn have achieved something the world thought impossible: manufacturing at a scale and precision that defies logic. However, their genius is largely Empirical and Process-oriented:
- The Yield King: TSMC’s "secret sauce" isn't necessarily a new theory of physics; it is the ability to manage a 1,500-step chemical process with zero errors. It is about statistical control and supply chain mastery.
- The Scaling Wall: As chips shrink to and below, the old "incremental" improvements stop working. You hit "Quantum Tunneling" (where electrons jump through walls they shouldn't).2𝑛𝑚2𝑛𝑚
- The Limitation: They are often the users of the science, not the authors of it. They rely on ASML (Dutch) for the light physics, Zeiss (German) for the optics, and Dassault (French) for the molecular simulation software.
2. Europe: The "Einstein Math" & Quantum Physics Layer
- eyond Geometry: Dassault Systèmes’ BIOVIA and SIMULIA tools don't just draw a battery; they simulate the Quantum Chemistry of the lithium ions. They are using "Einstein-level" mathematics to predict how materials will behave before those materials even exist in a lab.
- The "Physics-Informed" AI: While Asian digital factories are great at "Big Data" (looking at what happened yesterday to predict tomorrow), Europe is leading in Physics-Informed Neural Networks (PINNs). This is AI that is "pre-taught" the laws of gravity, thermodynamics, and electromagnetism so it doesn't have to "guess" like a standard chatbot.
- The ASML Example: ASML (Netherlands) is a prime example of "Basic Science" dominance. To make their latest machines, they had to solve the physics of Extreme Ultraviolet Light, which involves hitting a drop of tin with a laser twice to create a plasma as hot as the sun. That isn't "manufacturing"; that is applied high-energy physics.
3. The Shift: From "Supply Chain" to "Simulation"The Asian revolution was driven by the Supply Chain (moving atoms efficiently). The NVIDIA-driven revolution is driven by Simulation (replacing atoms with bits).
| Feature | The Asian Revolution (TSMC/Foxconn) | The European/NVIDIA Revolution (Dassault/Siemens) |
|---|---|---|
| Primary Driver | Economies of Scale & Labor Efficiency | Computational Physics & "Basic Science" |
| Focus | How to build it fast. | What is physically possible to build. |
| Core Asset | The "Mega-Factory" (Physical) | The "Virtual Twin" (Mathematical) |
| Scientific Deep-End | Materials Science & Nanography | Quantum Mechanics & Multi-scale Physics |
4. Why "Einstein Math" Matters Now
In the old world, you could build a car or a phone by "testing and breaking." In the new world of Deep Tech (Hydrogen fuel cells, Quantum computers, Carbon capture), the "Basic Science" is too complex for trial and error.
If you don't have the software (Dassault/Siemens) to simulate the Quantum fluid dynamics of liquid hydrogen, you cannot build the factory. This is why Europe is "reinventing" these sciences—they are building the mathematical "operating system" for a world that hasn't been built yet.
chris macrae
Feb 14