Neural Concept's Bootcamp tour 2025: Upskilling engineers for AI adoption

🤖 Wrapping up an inspiring #TdSE2025! Three days full of ideas, discussions, and innovation around the future of #SystemsEngineering - and we at AI Marketplace (KI-Marktplatz) were right in the middle of it! This year, our focus was clear: How can #AI – and especially Large Language Models – truly transform Model-Based Systems Engineering (#MBSE)? 🧩 1️⃣ “Can LLMs really do MBSE?”  Our colleagues Ruslan Bernijazov presented a systematic evaluation of how far today’s #LLMs can already support MBSE tasks - and where their limitations still lie.  The takeaway?  ➡️ LLMs show great potential for automation and knowledge integration,  but still require human expertise for consistency and reliability.  The path toward AI-supported Systems Engineering is open – but we’re only at the beginning.  ⚡ 2️⃣ AI-powered Systems Engineering with SysML 2.0  In our hands-on tutorial, Ruslan Bernijazov , Rik Rasor , and Thomas Meenken showed how Generative AI can help engineers automatically create SysML 2.0 models – from requirements to architecture. With live demos (including coffee machines ☕️, e-scooters 🛴, and drones 🚁), participants experienced how #AI and #MBSE can work hand in hand to boost efficiency and creativity. 💡 Our takeaway from #TdSE2025:  AI won’t replace Systems Engineers – but it will empower them.  A huge thank you to everyone who joined our sessions, shared their perspectives, and pushed the discussion forward. 🙌 Thanks to Gesellschaft für Systems Engineering (GfSE) e.V.   Our Speakers: Ruslan Bernijazov, Rik Rasor, Roman Dumitrescu See you next year!  Interested in Ai Marketplace? 💬 Comment “DEMO” or DM us to experience AI-powered Systems Engineering in action #AIMarketplace #ArtificialIntelligence #AIinEngineering #MBSE #SysML #GenerativeAI #SystemsEngineering #Innovation #Paderborn #OWL 

Loud thinking 🤔 So many competencies and diverse skills are becoming increasingly valuable – yet workflows and what we’re capable of are evolving rapidly. Blending design for manufacturing with algorithmic thinking, mathematics, statistics, software programming, kinematics, dynamics, contact mechanics, FEM, CFD, and intent-driven Dx/Dy/Dz design – all powered by AI – is now producing tangible component breakthroughs. AI accelerates both speed and scale, while remarkable collaboration between top-tier engineers, mathematicians, data scientists, and toolmakers delivers the deep content and insight required. Finding one better solution for a single component or system is, fundamentally, not far from solving a thousand 😄. The dilemma between development time and quality has been fundamentally transformed. It’s both fun and deeply inspiring to be part of this shift. Huge thanks to the nearly 50 remarkable individuals — spanning all kinds of skills and backgrounds — who are expanding the boundaries of what’s possible.

Engineers drawn to deep tech tend to share one trait : curiosity that goes beyond the surface. They want to understand why something works, not just how to make it run faster. Whether it’s simulation, AI-driven manufacturing, robotics or advanced control systems, the problems are rarely straightforward - and that’s exactly the appeal. The engineers who thrive in these environments blend disciplines: software, physics, maths and systems thinking. They’re comfortable reasoning from first principles and building solutions where no playbook exists. I spend a lot of time speaking with teams and engineers working in this space — people solving genuinely hard problems in deep-tech companies. If you’re curious about which teams are doing the most interesting technical work right now, or just want to stay connected to this world, I’m always happy to share what I’m seeing. #DeepTech #Engineering #AI #Simulation #Cplusplus #Manufacturing #Innovation

Loud thinking 🤔 So many competencies and diverse skills are becoming increasingly valuable – yet workflows and what we’re capable of are evolving rapidly. Blending design for manufacturing with algorithmic thinking, mathematics, statistics, software programming, kinematics, dynamics, contact mechanics, FEM, CFD, and intent-driven Dx/Dy/Dz design – all powered by AI – is now producing tangible component breakthroughs. AI accelerates both speed and scale, while remarkable collaboration between top-tier engineers, mathematicians, data scientists, and toolmakers delivers the deep content and insight required. Finding one better solution for a single component or system is fundamentally, not far from solving a thousand today😄.

In the first and second sessions of our Engineering AI Bootcamp, experts from SimScale, Convion, PTC, and nTop explored how to move AI from a small pilot to a core engineering strategy. We covered the State of Industrial AI, common bottlenecks like prototype costs, and a great customer spotlight from Convion on accelerating optimization with Physics AI. In Session 2, Jon Wilde (SimScale) and Brian Sather (nTop) shared insights on how to actually scale AI. They broke down the two-pronged solution required: 🤖 Engineering AI (LLMs): To crush the setup and lead time bottleneck. ⚡ Physics AI (GNNs/PINNs): To crush the computation bottleneck. We also demoed how AI Agents are becoming the new "UI" for non-experts and how to solve the critical data bottleneck for training. 🚀 But we're not done yet. Session 3 is just around the corner. Join us live on November 18th for Session 3: Building the Future: AI-Native Engineering Workflows. We'll be discussing the 2026 AI roadmap with: Matthias Bauer - Director of Software Development (Autodesk) Ram Seetharaman - Head of AI and Product Manager (Synera) David Heiny - CEO and Co-Founder (SimScale) This is your chance to see how generative and agentic AI will automate entire design and business processes. Missed the first two? Catch all the recordings AND register for the finale on one page: https://hubs.ly/Q03Sbb_z0 #AI #EngineeringAI #Simulation #DigitalTransformation #CAE #Bootcamp #FutureOfEngineering #GenerativeAI #AIagents

𝐌𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 𝐟𝐫𝐨𝐦 𝐭𝐡𝐢𝐬 𝐰𝐞𝐞𝐤: 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬 🖥️ Earlier this week, I attended the #MATLABEXPO by MathWorks in Munich, where I focused primarily on #SystemsEngineering, which is highly relevant to my team and product development. I gained actionable takeaways and met my former colleague Sara-Kristin, who now works at MathWorks. MATLAB/Simulink is a powerful and comprehensive platform that covers a wide range of areas, from the simulation of electrical systems to the training of neural networks and more. The event was a perfect chance to dive deeper into model-based development, exchange ideas, and connect with industry experts. I spent a day full of inspiring presentations and left with fresh input and clear validation that our focus on digital twins and model-based approaches is right. In particular, the talks by Dr. Jens Dietrich and Dr. Matthias Braband stood out and underscored how investing in virtual development shortens time to market, increases flexibility, and helps us deliver exactly what customers need. My key takeaway as an R&D manager is that we will connect our Systems Engineering approach more closely with our Simulink digital twins. I’m convinced that events like this are essential. They give us space to learn, challenge our thinking, and see the bigger picture behind our daily work. 💬 How do you keep yourself and your team inspired to learn and grow in such a fast-changing tech world?

I spent my Saturday at the Context Engineering Workshop with The Gen Academy, and here’s the thing. Some sessions simply teach you something. This one actually shifted how I think. It was all about Prompt Engineering until I heard about Context Engineering. So what is it ?? Context engineering sits at the heart of building reliable AI systems, but very few people break it down in a way that’s practical, structured, and immediately usable. This workshop did exactly that. Huge thank you to Aishwarya Srinivasan, Arvind Narayanamurthy, and the entire Gen Academy team for creating a space where learning feels both rigorous and accessible. I walked away with frameworks I can apply right away, along with a sharper mental model for how to design context in a way that elevates performance, clarity, and trust. For anyone exploring agentic workflows, retrieval, or prompt engineering at a deeper level, the teaching and the materials are an amazing source for both technical and non-technical folks. Grateful to have been part of this one and excited to keep sharpening this skillset. #ai #contextengineering #machinelearning #agents #llms #genai #community #learning #sanfrancisco #sftech

Loud thinking So many competencies and diverse skills are becoming increasingly valuable – yet workflows and what we’re capable of are evolving rapidly. Blending design for manufacturing with algorithmic thinking, mathematics, statistics, software programming, kinematics, dynamics, contact mechanics, FEM, CFD, and intent-driven Dx/Dy/Dz design – all powered by AI – is now producing tangible component breakthroughs. AI accelerates both speed and scale, while remarkable collaboration between top-tier engineers, mathematicians, data scientists, and toolmakers delivers the deep content and insight required. Finding one better solution for a single component is, fundamentally, not far from solving a thousand 😄.

Here is the inside of an AI Agent: Three core components: 1. Planner - Takes care of breaking down a task into individual steps. It's capable of taking feedback about the plan and generating a new version of it. 2. Evaluator - Takes care of evaluating a plan and providing feedback about it to the Planner component. It can also check the results of a task to determine whether they align with the plan. 3. Executor - Takes care of executing individual steps of a plan. All three components have access to tools and memory to do their job, I'm covering all of this in my course. It starts Monday. You can join at "ml dot school" for lifetime access to the best engineering program you'll take online.

We’re excited to launch "Test Lab: Innovation Research" Series, where TTC Global team members share real experiments and discoveries from the frontlines of AI-driven quality engineering. It can be extremely difficult to cut through the constant stream of hype about AI’s capabilities to see what really works and hold true promise. That’s why in this series we will be showcasing the results of real experiments and what they mean for customers. Here’s a taste of what’s to come: • AI agents that convert test frameworks between programming languages • Automation tools that generate test cases and detect accessibility issues • Efficiency gains up to 37% with proper AI-human collaboration • Honest lessons learned: where automation shines and where human expertise still leads At TTC Global, we believe innovation only matters when it drives real-world results. Stay tuned as we reveal how our experiments with Playwright MCP, GitHub Copilot, and Claude are reshaping the way we test, validate, and deliver quality at speed: https://lnkd.in/gE8SBQ_E Questions? Contact our NZ team's GM of Innovation and Technology Mei Tsai! #AI #AItesting #TechInnovation #TTCGlobal