PLM news, announcements and industry trends.

DEVELOP3D · Oct 23, 2026

CAD, CAE & Simulation · Partnerships & Acquisitions

AI-generated summary: Formula E has partnered with Comsol to use multiphysics simulation and computer-aided engineering to investigate the technological challenges of electric racing. The collaboration will support the all-electric championship’s technology teams in understanding how interacting physical phenomena affect race-car systems operating under extreme constraints. The work covers electrical, thermal, structural, electromagnetic, fluid and battery systems. Rather than examining these disciplines solely in isolation, Comsol will provide simulations to help Formula E understand their interactions. Comsol senior vice-president of product management Bjorn Sjodin describes electric racing as a practical example of the need for multiphysics simulation because several physical phenomena are continuously influencing one another. Comsol Multiphysics will be used directly with Formula E’s technology team to address these engineering challenges. The partnership will also explore simulation applications and digital twins as ways to make physics-based insights accessible beyond traditional modelling workflows. Formula E chief technology officer Dan Cherowbrier identifies AI-assisted modelling, agentic workflows and digital twins as particular areas of interest. He presents these approaches as potential ways to help engineers interact with models, explore designs and access simulation results more readily, rather than reporting capabilities already demonstrated through the partnership. The collaboration comes as Formula E prepares for its GEN4 era, when the next generation of race cars is expected to impose greater demands on performance, efficiency and energy management. The article illustrates the simulation context with a mesh of the GEN4 car in Comsol Multiphysics, but does not provide specific simulation results, quantified benefits or implementation milestones. Separately, Comsol has announced Comsol Multiphysics version 2027, including dedicated electric motor modelling capabilities relevant to advanced engineering workflows. The article does not specify how those new capabilities will be deployed within Formula E. Overall, the partnership’s stated focus is on improving understanding of coupled engineering problems and exploring broader access to simulation insight; measurable racing or development outcomes remain unreported.

Engineering.com · Oct 22, 2026

CAD, CAE & Simulation · Industry Trends

AI-generated summary: XR adoption in engineering has been constrained less by headsets themselves than by workflows that simplify CAD models to fit their limited computing power, argues Florian Haspinger, cofounder and managing director of Hololight, in an opinion article for Engineering.com. Immersive reviews can bridge the gap between viewing designs on a flat screen and building expensive physical prototypes. However, complex engineering models often exceed a headset’s rendering capacity. Reducing their complexity can remove precisely the details reviewers need to understand a design in space. Haspinger’s central argument is that rendering should move off the headset rather than requiring engineers to compromise the model. The article traces immersive engineering review back to the CAVE, developed at the University of Illinois Chicago in 1992. This room-sized installation projected imagery onto its walls, floor and ceiling. Viewers wore LCD shutter glasses with a position sensor, allowing the projections to adjust to their perspective as they moved. Rendering took place on the system driving the projectors, not on the glasses. Major automotive and aerospace companies led adoption, but high costs and substantial space requirements limited accessibility. Because the computing infrastructure was tied to a particular room, reviews also remained location-dependent and could require travel. Portable, lower-cost XR headsets addressed some of those access problems without eliminating model preparation. Both earlier systems and headsets required engineers to perform tasks such as tessellating geometry, reducing polygon counts and removing hidden parts. Haspinger emphasizes that the resulting review asset is a derivative rather than the model of record. Preparing it takes time, while the original CAD design continues to evolve. Consequently, the version available for review can lag behind current engineering work, and transferring annotations and identified problems back into the original data adds further versions to manage. This preparation also creates uncertainty about review findings. When reviewers identify an apparent issue, they may not know whether it exists in the original design or was introduced by simplification. Haspinger argues that this weakens confidence in engineering decisions and helps explain why some teams abandoned XR in favor of conventional screen-based reviews, where they could at least display the complete model correctly. In his account, successive generations of immersive technology changed the hardware but retained a recurring compromise between accessibility, model fidelity and review reliability. XR streaming offers an alternative architecture. The application containing the model continues running on its existing computing system, which renders the design at full complexity. Pixels are streamed to the headset while the original geometry remains where it is. Haspinger argues that this allows teams to review an assembly as soon as it is modeled, including materials and lighting, without preparing a simplified asset or reconciling a separate review version afterward. The claimed benefits include same-day reviews of revisions, faster iteration and reduced reliance on costly physical prototypes. Streaming also supports remote participation, potentially reducing travel across teams and locations. Spatial presentation can help customers and other stakeholders unfamiliar with CAD understand a design more quickly than a two-dimensional display, improving communication and helping expose issues before they become late-stage changes. However, the approach shifts technical requirements rather than removing them: organizations must establish what the connection between the rendering machine and headset needs before scheduling a review. Rendering hardware must also be adequate for the application, model complexity and number of concurrent sessions. Haspinger acknowledges that XR is unnecessary for some workflows and review stages. His conclusion is that streaming can make it an on-demand review tool, organized around a design question rather than hardware limitations. For engineering data management, the key argument concerns maintaining fidelity to the actual design and avoiding derivative-version reconciliation. These benefits are presented as the author’s position, not as results from comparative testing or quantified deployments. His company, Hololight, provides industrial XR infrastructure that separates software from headset hardware and streams pixels rather than model data.

Autodesk · Oct 22, 2026

CAD, CAE & Simulation

A new Instance of Autodesk Inventor opens when, despite one active session was already running: • Double-clicking on Autodesk Inventor file (IPT, IAM, IDW, DWG, IPN) in Windows Explorer or Autodesk Vault Client. • Import CAD (STP, IGES, PRT, STEP...) files. • Opening a file by Autodesk Vault Explorer. • Opening file with "View in Window" in Autodesk Vault Client. Other symptoms: • After registration DWG file to AutoCAD DWG Launcher, it starts to open as DWG TrueView new instance. Solution: Use one of the following: To open other CAD files(STEP, IGES ,PRT,STP) without starting additional Autodesk Inventor instances: 1\. Start Autodesk Inventor. 2\. Locate the Siemens NX part file in Windows File Explorer. 3\. Drag the PRT file onto the title bar of the open Autodesk Inventor window. 4\. The file is opened in the existing Inventor instance. Make sure the file types (.IPT, .IAM, .IDW, .IPN) are associated with the correct version of Autodesk Inventor: 1. Locate any .ipt (or .iam, .idw) file within File Explorer. 2. Right Mouse Button Click it. 3. Choose "Open With..." from context menu. 3. Even if it already set to open with Inventor, scroll down and select "More Apps" option. 4. Flag "Always use this app to open. files". 5. Choose "Look for another application on this PC". 6. Browse to the C:\\Program Files\\Autodesk\\Inventor 20XX\\Bin and select Inventor.exe. 7. Restart Inventor and Vault to check if it opens within the same instance. Make sure the file type .DWG (even Inventor .DWG) associated with AutoCAD DWG launcher: Note: More information, see DWG file type associativity in Windows for AutoCAD. 1. Right Mouse Button click Inventor .DWG drawing. 2. Press "Open with...". 3. Select a new app icon or "Choose another app". 4. Flag "Always use this app to open.dwg files" text menu. 5. Click "Choose another app". 6. Choose "AutoCAD DWG Launcher" and Press OK. Note: If after registration to Launcher it started to open a new instance of DWG TrueView, then edit the Windows registry. See Double-clicking or opening a DWG file starts a new instance of AutoCAD product. Changing registration with Registry Keys: | Warning: Problems caused by improperly editing the Windows registry could render your computer operating system unusable. Microsoft® provides a wealth of critical information that you need to know about the registry in the Microsoft® Knowledge Base at Miscrosoft Support. Use the Microsoft® Registry Editor only at your own risk and only after backing up the registry as well as the system.dat and user.dat files. As outlined for your operating system in the Microsoft Knowledge Base and in the related solution, TS66513. Additional information about the registry is also contained in the Help topics in the Microsoft® Registry Editor. | If all the previous steps did not help, this behavior could be caused by an improper setting of the following Registry Keys: 1. In the Start menu of Microsoft Windows, type regedit and press Enter on keyboard. 2. If the following window shows up, click Yes. 3. Go to the following path: ``` HKEYCLASSESROOT\Inventor.PartDocument/SHELL/open/command ``` 4. Under this key, the (Default) value should be showing the path to Inventor.exe with /dde at the end. The value should be similar to this string: C:\\program files\\Autodesk\\InventorVersion\\bin\ inventor.exe /dde ``` HKEYCLASSESROOT\Inventor.PartDocument/SHELL/open/ddexec ``` Under folder "ddeexec", the (Default) string key should be set to: \[open("%1")\] Note: Other files types, like Assembly .IAM, can also contain the wrong values in related registry keys and should be changed with similar steps. Autodesk Inventor sets automatically registry values on Start-up • Autodesk Inventor Assembly .IAM ``` HKEYCLASSESROOT\Inventor.AssemblyDocument\SHELL\open ``` • Autodesk Inventor Drawing .IDW ``` HKEYCLASSESROOT\Inventor.DrawingDocument ``` • Autodesk Inventor Part .IPT ``` HKEYCLASSESROOT\Inventor.PartDocument ``` • Autodesk Inventor Presentation .IPN ``` HKEYCLASSESROOT\Inventor.PresentationDocument ``` Note: To solve the issue, sign in as the Local Administrator of the machine and start Autodesk Inventor. Otherwise, with a Local Administrator user, modify this Registry Keys as explained earlier. In case, after having applied the steps above, the issue still persists, try to change this in the registries: 1. Browse to: 2. HKEY\CURRENT\USER\\Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\FileExts\\.ipt 3. Delete the subfolder “ UserChoice” from the above registry branch. 4. Repeat the procedure for any file type. Change extension as appropriate. • Reset to the Microsoft recommended default apps: 1. Go to Windows Settings (Windows Start-> Settings). 2. Select Apps from the main menu. 3. Choose Default Apps (Options on the left side). 4. Click on RESET button. After assigning or resetting the file types in Windows try to set the Open File Behaviors in the Autodesk Vault Client for Autodesk Inventor file types and DWG file types to Default by following these steps: • 1. In the Autodesk Vault Client > Click Tools > Options. 2. Click Settings... > Open file behavior settings option. 3. Change the Behavior for Inventor and AutoCAD > Default > OK. 4. Confirm the changes with OK. If all above solutions don't work consider creating a new "Windows local admin account profile": For information about create a new Windows local admin account profile, see Create a local user or administrator account in Windows. If that does not work, perform a clean uninstall and reinstall of Autodesk Inventor and Autodesk Vault: For information about how to do a clean uninstall and reinstall for Inventor and Vault, see Perform a clean uninstall of Inventor. Note: As this is a regular Microsoft Windows mechanism (DDE), it may happen that "sometimes" a new instance of a process is created (instead of using the existing one) when the process is not able to answer the internal Windows request. This can happen when the application is busy with long-lasting tasks. So, avoid using double-click action in Windows Explorer or Autodesk Vault if the application that should open the file is unresponsive. Wait for the application to react again and then open the file by double-click. See Also: • How to Re-Regestring Inventor • How to re-register Inventor via Command Prompt • How to create a local user or administrator account in Windows 10 • Miscrosoft Support • Inventor starts a new instance when opening a file from the Vault client • New instance of Inventor read-only mode opened when opening another file from Vault Products: AutoCAD; Inventor Products; Vault Products; Was this information helpful? YesNo Need help? Ask the Autodesk Assistant! The Assistant can help you find answers or contact an agent. Ask the Assistant What level of support do you have? Different subscription plans provide distinct categories of support. Find out the level of support for your plan. View levels of support ADSK Web Analytics Foundation Cross-Domain Solution Web Analytics Foundation Analytics Domain Solution (WADS) ... DCOM based Universal Assistant

Autodesk · Oct 22, 2026

CAD, CAE & Simulation

Autodesk and the Autodesk logo are registered trademarks or trademarks of Autodesk, Inc., and/or its subsidiaries and/or affiliates in the USA and/or other countries. All other brand names, product names or trademarks belong to their respective holders. Autodesk reserves the right to alter product and services offerings, and specifications and pricing at any time without notice, and is not responsible for typographical or graphical errors that may appear in this document. © 2026 Autodesk, Inc. All rights reserved. Most read • How Autodesk Forma and AI are advancing a more connected future for AEC • Autodesk and the University of Florida open the most advanced robotics industrialized construction lab in the US to tackle housing and labor shortages • Autodesk launches Support for Small Business and expands tools to help small teams save time, manage costs, and strengthen security Related content サステナビリティ 24 Aug 2026 つながるワークフローが実現した、ポートランド国際空港の新たな姿 米国 Autodesk 社（本社：米国カリフォルニア州／プレジデント 兼… Autodesk PR サステナビリティ 21 May 2026 Autodesk、世界的サステナビリティランキング「Global 100」 2026 年版で第 89 位、10 年連続選定 〜新指標「サステナブル収益モメンタム」で変革のスピードも評価〜 米国 Autodesk 社（本社：米国カリフォルニア州／プレジデント兼… Autodesk PR 24 Feb 2026 Autodesk、米国パラリンピアンで BioDapt 社 CEO 兼創設者のマイク・シュルツ氏との、次世代義肢の開発に向けた提携を発表 Autodesk とともに、シュルツ氏と彼の会社 BioDapt は 2028… Autodesk PR

Autodesk · Oct 22, 2026

CAD, CAE & Simulation · Partnerships & Acquisitions

Autodesk announced plans to integrate NVIDIA technologies at NVIDIA’s GTC Berlin. Bentley uses Autodesk NavPack to integrate predictive engineering insights into its vehicle design workflow. Autodesk is extending Autodesk NavPack, which helps automotive designers bring predictive engineering insights into the vehicle design process. The planned integration will connect NavPack with NVIDIA PhysicsNeMo and NVIDIA Warp to develop additional AI model architectures and GPU-accelerated workflows. In automotive workflows, designers use Autodesk Alias to create and refine vehicle geometry. NavPack uses predictive AI models trained on a customer’s existing simulation data to help designers understand how design changes could impact vehicle performance. This means a designer can evaluate aerodynamic implications while continuing to explore and iterate on a vehicle’s shape in Alias, while full-fidelity simulation remains part of model training and final validation. “Designers make better decisions when engineering insight comes in the ideation phase, not the final product state,” said Thomas Heermann, vice president, Automotive Design at Autodesk. “Aerodynamic prediction is just the beginning. With NavPack, we can transform Alias into an environment where critical engineering and manufacturing knowledge informs design from the very start. By bringing downstream insight upstream—while designers are still exploring and shaping ideas—we enable teams to make better-informed decisions faster, reduce iteration costs, and create designs increasingly optimized for real-world performance and production.” This is industrial AI in practice: bringing predictive engineering insight earlier into the design process so teams can make more informed decisions about the products they design and make for the physical world. Autodesk’s planned integration of NVIDIA libraries is intended to help advance that vision by expanding the model architectures supported by NavPack and enabling GPU-accelerated parts of the workflow for customers with compatible NVIDIA hardware. Most read • How Autodesk Forma and AI are advancing a more connected future for AEC • Autodesk and the University of Florida open the most advanced robotics industrialized construction lab in the US to tackle housing and labor shortages • Autodesk launches Support for Small Business and expands tools to help small teams save time, manage costs, and strengthen security Related content Product Design & Manufacturing 15 Sep 2026 Autodesk advances AI for design and manufacturing at AU 2026 How AI in design and manufacturing can automate workflows and help engineers make better decisions. Jeff Kinder Our Company 15 Sep 2026 Closing the gap between what we can imagine and what we can build Autodesk University highlights Autodesk AI across design, make, and operate workflows. Andrew Anagnost Tech Innovation 20 Jul 2026 How data and AI are transforming manufacturing workflows Manufacturing is entering a new era where AI, connected data, and intelligent workflows are… Srinath Jonnalagadda

Siemens Digital Industries Software · Oct 22, 2026

CAD, CAE & Simulation

As products become more complex and development timelines continue to shrink, NVH engineers are moving beyond the traditional “Build → Measure → React” approach toward a new workflow: Characterize → Predict → Explore . By combining invariant component characterization, blocked forces, and dynamic substructuring techniques, engineers can create hybrid virtual prototypes that predict system-level NVH performance before physical prototypes exist. This shift enables earlier design exploration, reduces reliance on late-stage troubleshooting, and accelerates the transition to virtual prototyping. The 2027 component-based Transfer Path Analysis & Virtual Prototyping Masterclass is designed to help engineers adopt these methods through hands-on training in the latest techniques and workflows available in Simcenter Testlab. Build → Measure → React For a long time, this has been the mantra of NVH engineers. If you wanted to know what the NVH performance was of a particular product configuration or design, you had to build a physical prototype, measure its performance in operation, and finally decide what to do with the results. Today, that approach is no longer sustainable. Products are becoming more complex while development time budgets are simultaneously shrinking. That is why NVH engineers across all industries are shifting to a new mantra: Characterize → Predict → Explore This new approach exploits recent advancements in invariant component characterization using blocked forces and virtual assembly using dynamic substructuring techniques. Component data from test and simulation is combined in a hybrid virtual prototyping workflow to predict system-level NVH performance at any stage of development. As a result, NVH engineers can replace late-stage troubleshooting on physical prototypes by early-stage design exploration using virtual prototypes instead. At Siemens, we want you to succeed in adopting this new NVH development approach. That is why we are excited to announce the 12 th edition of the Component-based Transfer Path Analysis & Virtual Prototyping masterclass , taking place on March 16–19, 2027 at our Simcenter Testing Solutions headquarters in Leuven, Belgium . Agenda & registration What is this masterclass? The Component-based Transfer Path Analysis (C-TPA) & Virtual Prototyping masterclass is a 4-day live event that is designed to equip you with the knowledge, skills and tools required to navigate the transition from physical prototypes to virtual prototypes in NVH development. Siemens will bring you up to speed on the fundamentals and latest advancements in hybrid virtual prototyping methodology, with expert techniques and best practices for a wide range of industry application cases. The methodology will then be put into practice with over 9 hours of hands-on workshops combining physical measurements and data processing in Simcenter Testlab . This format offers you first-hand experience and practical insights that are immediately applicable in your own NVH engineering workflows. In addition, this masterclass offers an opportunity to learn from industry leaders and network with peers. We have invited 3 guest speakers to share their experience and advice in adopting hybrid virtual prototyping methodology. This will be the 12 th edition in a series of worldwide masterclasses that have already been attended by over 300 engineers from 18 countries . See for yourself how past attendees described the experience in their own words : What is new in 2027? Siemens is rapidly expanding its offering for hybrid virtual prototyping in Simcenter Testlab with every new software release. This edition of the masterclass will cover several exciting new capabilities, such as: • Efficient FRF testing using Shaker Acquisition in Testlab Neo • Reciprocal FRF measurements in space-constrained conditions using Simcenter Qsources acoustic exciters • Automated characterization of component blocked forces and interface FRFs using Automated component model extraction (AutoCMX) • Smooth and targeted stabilization of force estimation via Matrix Inversion using new regularization methods • Rapid component modification and design exploration in Virtual Prototype Assembly using Sensitivity Analysis • Immersive real-time auralization of virtual prototypes using Testlab NVH Simulator This edition will also introduce a new optional fourth half-day for attendees who want to learn about next-level techniques for hybrid virtual prototyping in Simcenter Testlab, such as: • Component FRF modeling in impossible boundary conditions using FBS Decoupling • Evaluating source-receiver contributions based on complex and injected powers • Implementing an automated hybrid virtual prototyping process • Integrating hybrid virtual prototyping into AI workflows and agents Ready to participate? This masterclass emphasizes interactive learning with hands-on workshops using dedicated hardware and software setups. Therefore, seats are strictly limited to ensure the best possible learning experience for each participant. Are you ready to embark on the industry shift towards hybrid virtual prototyping? Then don’t miss this opportunity to equip yourself with the knowledge, skills, and tools needed for the journey! Agenda & registration See you in Leuven? Simcenter physical testing Simcenter SCADAS ES Simcenter Testlab

Siemens Digital Industries Software · Oct 22, 2026

AI in Product Development · CAD, CAE & Simulation

Siemens today announced an expansion of its EDA agentic AI portfolio, developed in collaboration with NVIDIA. The latest updates combine the self-verifying agentic AI capabilities introduced at DAC 2026 with NVIDIA accelerated computing to address one of advanced packaging's most demanding hurdles: electrical, power, thermal and codesign for 3D ICs. The new capability orchestrates Siemens’ electrothermal analysis portfolio into a single autonomous workflow. By leveraging Siemens’ Design Space Exploration and Optimization (DSE/DSO) technology, the workflow allows engineers to evaluate and validate hundreds of design alternatives against signoff-grade, physics-based solvers. In benchmark studies, this approach reduced engineering effort by up to 95 percent (reducing a multi-week process to a matter of hours), cut workflow setup time by up to 99 percent (typically, less than five minutes versus several days across seven tools) and enabled up to 50x greater design exploration coverage compared to conventional manual flows. "Electrothermal codesign has traditionally required experts to coordinate multiple tools and manually iterate through a limited set of design alternatives," said Ankur Gupta, executive vice president, EDA IC Portfolio, Siemens. "By combining agentic AI with Siemens’ proven physics-based analysis, engineers can now evaluate up to 50 times more design scenarios, enabling faster and more confident decisions for the next generation of AI infrastructure." “Power delivery and heat dissipation increasingly determine what’s possible in 3D chip design,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “With NVIDIA Agent Toolkit and Nemotron-3.5-Lightning, Siemens’ agents coordinate electrical and thermal solvers to deliver up to 50x greater coverage in design space exploration with 95% less engineering effort. By diagnosing how power delivery, die placement and package materials contribute to missed targets, these agents make electrothermal analysis an integral part of chip development.” AI factories are redefining the chip AI factories are redefining what the silicon inside them must deliver: more compute, more memory bandwidth and dramatically improved thermal performance - all within an ever-smaller physical footprint. NVIDIA’s Rubin GPU illustrates this scale, pairing up to 50 petaflops of NVFP4 inference with 288 gigabytes of HBM4 per GPU that scales up into larger co-designed systems such as NVIDIA Vera Rubin NVL72. As compute and memory converge in these tightly integrated packages, power density rises and heat becomes concentrated. Electrothermal codesign can no longer be a late-stage check; it must become a first-order design requirement. The Challenge: Manual complexity Today, solving these challenges requires that specialists manually coordinate workflows across multiple tools, transfer data between domains and repeatedly iterate between electrical, power, thermal and extraction analyses. As complexity increases, engineers are often forced to limit the number of scenarios they evaluate given time and compute constraints, increasing the risk that critical bottlenecks remain undiscovered until late in the design cycle. The Solution: Agentic 3D IC engineering workflow Siemens is introducing a new model. An engineer defines the design objective and the Fuse EDA AI Agent - integrated with Intelligence Center X - orchestrates multiple Siemens tools. The agent runs coupled electrothermal analyses, identifies missed targets and diagnoses whether the root cause lies in power delivery, placement, materials or a combination of all three. The agent uses analysis results to guide design-space exploration, evaluate tradeoffs and rerun promising configurations against the engineer’s targets. This iterative cycle helps teams assess more alternatives and identify power and thermal issues earlier in development. This workflow autonomously orchestrates nine steps across seven of Siemens’ EDA tools, executing 50 to 100 reasoning steps over an 8 to 10 hour runtime. It allows teams to sweep heat transfer coefficient (HTC) boundaries, power budgets and material stack-ups across 200–500+ design points per cycle. The integrated tool chain includes: • Innovator3D IC™ Integrator software: Floor planning and complete 3D IC assembly and package design. • Solido™ Characterizer software: AI-powered library characterization to generate Liberty files. • mPower™ Digital software: Power analysis and temperature-dependent instance power calculation. • Calibre® 3DThermal software: 3D IC thermal signoff and power mapping. • Simcenter™ Flotherm™ software: System-level thermal simulation and visualization. • Calibre® xACT software: Temperature-dependent parasitic extraction. • Solido™ SPICE software: AI-accelerated power and temperature-aware electrical SPICE simulation. Leveraging NVIDIA AI Siemens combines NVIDIA Agent Toolkit with NVIDIA accelerated computing and CUDA-X libraries to support agent-driven engineering workflows. Accelerated simulation tools give agents faster results to evaluate, helping engineers explore more design scenarios within the available time. • Nemotron open models: Used for orchestration and execution of agentic EDA subflows. • NeMo Gym: For tracking agent trajectories, and finding hot spots to refine agent skills and MCP tool descriptions • NeMo RL: For post-training fine-tuning of cost-efficient LLMs with domain-specific EDA knowledge and EDA tool-calling proficiency. • NeMo Relay: Provides observability and auditability across agent harnesses. • NeMo Switchyard: Intelligently routes tasks to balance compute accuracy utilization and token cost. • NVIDIA OpenShell: Securely sandboxes agent execution to protect sensitive intellectual property and foundry PDKs. • NVIDIA CUDA-X libraries: Accelerate supported engineering solvers used by the agents, reducing simulation wait time across iterative design workflows. Availability Additional agentic workflows and capabilities from across the Siemens EDA portfolio - spec-to-RTL signoff with Catapult and Questa, design-for-test with Tessent, RTL-to-GDS with Aprisa and Calibre, and custom IC verification with Solido and Calibre - will be showcased at NVIDIA GTC Berlin . To learn more about Siemens’ agentic EDA workflows, please visit: https://www.siemens.com/en-us/products/fuse-eda-ai-system/agent/ Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens' software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today's ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation. Siemens Digital Industries (DI) empowers companies of all sizes within the process and discrete manufacturing industries to accelerate their digital and sustainability transformation across the entire value chain. Siemens’ cutting-edge automation and software portfolio revolutionizes the design, realization and optimization of products and production. And with Siemens Xcelerator – the open digital business platform – this process is made even easier, faster, and scalable. Together with our partners and ecosystem, Siemens Digital Industries enables customers to become a Digital Enterprise. Siemens Digital Industries has a workforce of around 70,000 people worldwide. Siemens AG (Berlin and Munich) is a leading technology company focused on industry, infrastructure, mobility, and healthcare. The company’s purpose is to create technology to transform the everyday, for everyone. By combining the real and the digital worlds, Siemens empowers customers to accelerate their digital and sustainability transformations, making factories more efficient, cities more livable, and transportation more sustainable. A leader in industrial AI, Siemens leverages its deep domain know-how to apply AI – including generative AI – to real-world applications, making AI accessible and impactful for customers across diverse industries. Siemens also owns a majority stake in the publicly listed company Siemens Healthineers, a leading global medical technology provider pioneering breakthroughs in healthcare. For everyone. Everywhere. Sustainably. In fiscal 2025, which ended on September 30, 2025, the Siemens Group generated revenue of €78.9 billion and net income of €10.4 billion. As of September 30, 2025, the company employed around 318,000 people worldwide on the basis of continuing operations. Further information is available on the Internet at www.siemens.com . Note: A list of relevant Siemens trademarks can be found here . Other trademarks belong to their respective owners. Contacts for Press Siemens Digital Industries Software PR Team Email: press.software.sisw@siemens.com Further Reading Press Release all Siemens and NVIDIA advance AI-powered PCB inspection from Digital Twin to factory October 21, 2026 Press Release all Siemens extends next-generation data acquisition portfolio for modern physical testing with Simcenter SCADAS ES October 15, 2026 Press Release all Siemens transforms computational lithography for next-generation AI semiconductors with NVIDIA cuLitho October 14, 2026

NVIDIA Newsroom · Oct 22, 2026

CAD, CAE & Simulation

Companies around the world are using the NVIDIA DSX AI Factory Platform to plan, simulate and run AI factories as one system and unlock infrastructure development. October 21, 2026 by Vladimir Troy Companies around the world are building on the NVIDIA DSX AI Factory Platform to plan, simulate and run the grid and the AI factory as one system, from the substation to the rack. Their adoption of the platform responds to an acute problem. Power is now the biggest bottleneck in infrastructure. More than $300 billion in equipment is backlogged, and over 200 gigawatts of projects are waiting in U.S. interconnection queues. One root cause of the bottleneck is siloed systems. The grid, the facility and the compute inside have long been designed independently from one another, creating the conditions for delays, risk and stranded capacity. Read All NVIDIA in Europe Announcements To help address this, NVIDIA DSX brings these systems together: • NVIDIA DSX Sim and SimReady assets from leading energy manufacturers let engineers simulate the grid, substation and AI factory together, validating generators, switchgear, power systems and cooling before breaking ground. • NVIDIA DSX Flex syncs AI factory demand with grid conditions and onsite generation, turning AI factories from static loads into flexible grid assets. • NVIDIA DSX MaxLPS and DSX Exchange squeeze more compute from every watt and securely share signals across compute, networking, energy, cooling and operations. GE Vernova is deploying its grid-to-facility and rack power expertise to complement NVIDIA DSX systems, enabling customers to transform power constraints into competitive advantages while maximizing compute and token output, sustainability and grid reliability. Hitachi Energy ’s battery energy storage system has qualified as NVIDIA DSX Ready , helping customers identify power infrastructure that meets applicable NVIDIA DSX reference architecture design requirements for resilient, reliable and scalable AI factories. Schneider Electric , together with AVEVA and ETAP, is advancing NVIDIA DSX with validated NVIDIA Vera Rubin power and cooling reference architectures and an NVIDIA Omniverse -powered AI factory digital twin. Combining AVEVA ’s engineering software expertise with NVIDIA SimReady assets, OpenUSD and Omniverse enables connecting simulations, real-time operational data and AI-driven optimization across AVEVA, Schneider Electric and third-party solutions. Together, these capabilities help organizations design, simulate, operate and optimize data centers across their lifecycle, improving energy efficiency and workload performance while accelerating time to token. Siemens Energy is expanding its collaboration with NVIDIA by developing a reference design for resilient, scalable and grid-aware power infrastructure across power generation, transmission and distribution. This design is aligned with the requirements of the NVIDIA DSX Vera Rubin AI factory design. The company is also exploring usingNVIDIA technologies such as RAPIDS, Metropolis and Isaac Sim for its Noedra digital twin platform, evaluating how the technologies could enhance grid infrastructure monitoring, identify emerging risks and support unplanned outage prevention. Phaidra integrated DSX Flex, DSX Max-Q and DSX Exchange into its self-learning agents, optimizing cooling and power allocation to maximize tokens per watt, improve grid flexibility and deliver about 10% more compute within the same power envelope. Emerald AI integrated its Conductor platform into DSX Flex to help utilities tap underused grid capacity, an approach modeled to unlock up to 100 gigawatts on the existing U.S. grid. Visit Hitachi Energy (booth 7004, hall 7), Schneider Electric (booth 3041, hall 3) and Siemens Energy (booth 3048, hall 3) and tune into sessions with Schneider Electric on Wednesday, Oct. 21: From Grid to AI: Engineering the Future of AI Factories and Energy Intelligence: Powering the AI Era . Read All NVIDIA in Europe Announcements

CIMdata · Oct 22, 2026

PLM Software · AI in Product Development

Addressing the Needs of the Next Generation of PLM Professionals: New module covering AI in PLM added. Ann Arbor, Michigan, USA, 22 October 2026—CIMdata, Inc., the leading global strategic consulting and research firm focused on Product Lifecycle Management (PLM) and its digital transformation, announces that registration is now open for its upcoming PLM Certificate Program class, scheduled for December 7-11, 2026, in Ann Arbor, MI. This intensive, weeklong program equips PLM professionals with actionable strategic frameworks and high-level knowledge to formulate and execute effective PLM strategies. Participants gain expertise in addressing common challenges and take a deep dive into advanced topics—including the practical application of AI, IoT, and Digital Twins—through guided discussion and structured problem-solving sessions. This assessment-based program delivers a solution-neutral, personalized classroom experience, comprehensive individual and team-based exercises, and rigorous achievement evaluations. CIMdata experts, with decades of hands-on, industry-recognized expertise, guide participants through this five-day, eight-session program to help industrial organizations maximize the value of their PLM investments. Upon successful completion of the program and assessments, participants receive the prestigious CIMdata Certificate of PLM Leadership. CIMdata’s Edge in PLM Education With over forty years of industry leadership, CIMdata has a proven record of guiding industrial companies in developing and executing top-tier PLM and digital transformation strategies and tactics. Whether you're starting a new PLM journey or already implementing PLM, this program supports all levels of expertise. “As digital transformation initiatives become more complex and urgent, the need for high-impact PLM education has never been greater," said Cheryl Peck, CIMdata’s Chief Marketing Officer. “By incorporating a new AI in PLM module alongside decades of deep industry insight, CIMdata is uniquely positioned to help PLM professionals stay ahead, overcome evolving challenges, and succeed.” The CIMdata PLM Leadership Certificate is designed for PLM professionals at industrial organizations and solution providers who lead their company’s digital strategy. Upon successful completion of the assessment-based program, participants receive a Certificate of PLM Leadership and up to 3 CEUs, validating their expertise in strategic PLM practices. For more information on class schedules, detailed agendas, and registration, please visit https://www.cimdata.com/en/education/plm-certificate-program or contact Cheryl Peck at This email address is being protected from spambots. You need JavaScript enabled to view it. .

Beyond PLM · Oct 22, 2026

PLM Software

AI-generated summary: PTC’s launch of Jetstream gives engineering teams a way to capture design-review discussions alongside product data, but its longer-term significance depends on whether that context remains usable across companies, systems, and product changes. Writing in Beyond PLM, Oleg views the release as a practical step toward “Product Memory”: connecting product history with decisions, assumptions, evidence, and outcomes so future teams can understand why a design choice was made and whether its reasoning still applies. The article revisits questions raised after PTC Next 2026 in Chicago about intelligent PLM, collaboration, and context layers operating alongside existing systems of record. It also draws on Jon Stevenson’s observation that product data generally describes what a product is more clearly than why it became that way. Supplier constraints, quality concerns, and other contributions can materially change a design without leaving readily recoverable reasoning. PLM systems already support rationale in documents, issues, and change records; the problem is capturing enough of the actual discussion and preserving its connection to the product, particularly when contributors work outside engineering’s CAD and PLM environments. According to PTC’s product descriptions, Jetstream provides access-controlled Rooms where invited participants review 2D and 3D content in a browser. Feedback is associated with the relevant version, participant, time, and view, while AI summaries help reviewers follow discussions and next actions. Windchill remains the system of record, and Jetstream also accepts direct file uploads. These capabilities could reduce disconnected handoffs and the effort needed to reconstruct conversations. The more ambitious opportunity is to reuse review context when subsequent decisions arise. Oleg emphasizes Jetstream’s architecture as a multi-tenant cloud collaboration layer alongside existing systems of record. He contrasts this with Aras Innovator Visual Collaboration, which organizes discussions around Innovator items, while cautioning that similar viewing, commenting, and markup features do not establish architectural equivalence. Jetstream’s standalone Rooms appear to offer participation without requiring everyone to become a user of the host company’s PLM system. However, multi-tenancy does not itself demonstrate collaboration between independently controlled product environments, and shared Rooms may simply contain uploaded copies. He proposes testing an OEM using Windchill, a supplier using SolidWorks/PDM, and a partner using Altium 365: can each preserve authoritative records, source identities, revisions, permissions, and sharing control, and what happens to updates and retained context? A PTC demonstration illustrates why decision conditions matter. A supplier cannot provide a proposed 174 mm excavator pin but offers 170 mm or 175 mm alternatives, leading the engineer to adapt the assembly for 175 mm. In Oleg’s hypothetical continuation, a new supplier offers 174 mm pins two years later. That change could justify revisiting the original decision, but it would not invalidate the approved design: fit, strength, testing, maintenance, or other considerations might still support it. Useful Product Memory would expose the original constraint, alternatives, engineering evaluations, approvals, and subsequent evidence. The approved product definition would remain authoritative until responsible personnel assessed and approved a change. PTC describes exporting Room activity to PDF and checking it into Windchill, with associated product-data links and tracking of shared revisions. Oleg regards this as useful evidence retention, not proof of fully reusable lifecycle context. PDFs can contain meaningful configuration and approval information, and AI can extract it; the issue is whether other applications can reliably find decisions, establish their scope, follow supporting evidence, distinguish accepted outcomes from unresolved suggestions, and recognize superseded decisions. The launch materials establish a capture-and-retention workflow but do not demonstrate the full range of reuse by other applications or lifecycle agents. The author’s remaining tests concern downstream use in CAD, PLM, manufacturing, and service; openness across company boundaries; preservation of authority and access distinctions; and adoption in actual reviews. Supplier comments, AI summaries, and approved engineering decisions must retain their different status and responsibilities. Some reasoning will also continue to arise elsewhere. Oleg concludes that Jetstream is a useful step toward intelligent lifecycle context, while durable Product Memory remains an opportunity requiring further demonstration. He discloses that his role as OpenBOM co-founder and CEO shapes his perspective.