Thanks to everyone who joined the Rescale team at the Oasys LS-DYNA Environment UK Users’ Conference in Birmingham last week.
The conversations onsite made it clear that crash and safety teams are looking for practical ways to accelerate validation by connecting traditional simulation with machine learning.
During our session, we shared how engineering teams can now scale automated dataset creation and train high-accuracy surrogate models directly on the Rescale platform, compressing design exploration loops from days to near real-time.
We showcased a live workflow featuring 400 jobs orchestrated via API, model training completed in 24 minutes, and inference delivered in seconds. It is a powerful example of how simulation teams can eliminate the trade-off between speed and accuracy to make faster engineering decisions.
#Rescale #Oasys #LSDYNA #Simulation #CAE #CloudHPC #EngineeringAI
ALT Two people in business-casual attire stand smiling between Rescale and Oasys LS-DYNA Environment banners at an indoor event booth. The Rescale banner reads “AI-First Engineering” with bullets for Intelligent HPC, Advanced Modeling & Simulation, Agentic Digital Engineering, and AI Physics, plus the line “The Comprehensive Digital Engineering Platform Built for the AI Era, Accelerating Product Development and R&D” and “Start Your Journey with Rescale.” A nearby table holds a laptop with a blue screen and papers in a modern, wood-paneled venue.
ALT Conference room with attendees seated and facing two large screens displaying a presentation titled “Interior Head Impact Workflow: Integrating LS-DYNA & Machine Learning on Rescale Cloud HPC,” with Rescale and Oasys branding visible. Two presenters stand at the front, and a vertical sign with a QR code reads “Shaping the Future of.”
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#RSI CTO, Srikara Rao, shares his thoughts with @TheLeadDev about new roles being created in the age of AI.
Check the full article here:leaddev.com/ai/the-ai-talent…
#AIFirstCareers #EngineeringAI #FutureOfWork #AIJobs #TechCareers #EmergingRoles #WorkforceEvolution #TechLeadership
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Jun 15
📣 New Podcast! "Il progettista aumentato: come l'AI trasforma il CAD" on @Spreaker #autodeskfusion #automazione #caddesign #cloudcad #engineeringai spreaker.com/episode/il-prog…
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Jun 14
🔔 Why Discovery Models Need Goals, Feedback, Constraints, Validation, and Governance
linkedin.com/pulse/from-flue…
➡️ Engineering intelligence begins where fluent generation ends.
▪️ In high-consequence engineering, an AI-generated option must survive physical behaviour, load conditions, thermal effects, fatigue, corrosion, manufacturability, cost, regulation, lifecycle evidence, safety requirements, and accountable human judgement.
➡️ Generative AI gives engineers more options than ever.
▪️ But more options was never the engineering problem.
▪️ The real value is knowing which few possibilities survive the physics, the constraints, and the evidence.
➡️ In engineering discovery, fluent outputs are only useful when they can become validated, traceable, defensible decisions — decisions that can stand up to simulation, testing, lifecycle evidence, cost, safety, regulation, and accountable human judgement.
➡️ For engineers, researchers, and academics working with discovery models, the shift is clear: AI should not only widen the search space. It must help narrow it responsibly.
✅ This is the shift from fluent AI to evidence-ready engineering intelligence.
🌐 #EngineeringAI #DiscoveryModels #EvidenceReadyAI #PhysicsInformedAI #SimulationAndValidation #SystemsEngineering #AIGovernance #ComputationalEngineering #ResearchInnovation #AdvancedEngineering
ALT 🔔 Why Discovery Models Need Goals, Feedback, Constraints, Validation, and Governance https://www.linkedin.com/pulse/from-fluent-ai-evidence-ready-engineering-cuneyt-ozturk-807ve ➡️ Engineering intelligence begins where fluent generation ends. ▪️ In high-consequence engineering, an AI-generated option must survive physical behaviour, load conditions, thermal effects, fatigue, corrosion, manufacturability, cost, regulation, lifecycle evidence, safety requirements, and accountable human judgement. ➡️ Generative AI gives engineers more options than ever. ▪️ But more options was never the engineering problem. ▪️ The real value is knowing which few possibilities survive the physics, the constraints, and the evidence. ➡️ In engineering discovery, fluent outputs are only useful when they can become validated, traceable, defensible decisions — decisions that can stand up to simulation, testing, lifecycle evidence, cost, safety, regulation, and accountable human judgement. ➡️ For engine
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@grok My Last Fable 5 build
Tweet 1/5Stress-tested Forge — my fully-local offline engineering RAG pipeline — against a real 51-page NASA technical report (TN D-7243, 1973). Real OCR’d scan, noisy layout, tables, mixed formatting. No synthetic data. Full pipeline: OCR → layout analysis → chunking → hybrid retrieval → grounded generation. Here’s what survived and what didn’t 🧵
Tweet 2/5 What held rock solid:✅ 208/208 numeric values extracted and preserved without hallucinated digits or transcription errors ✅ Precise page-level citation mapping across all 51 pages (section → page grounding) ✅ Zero fabrication: when disambiguation failed on ambiguous identifiers, the system returned empty results instead of guessing
Tweet 3/5 Failure point:Identifier/part-number extraction missed entirely. Regex patterns were tuned to a different numbering scheme (common in aerospace docs). The fallback hybrid retriever (BM25 nomic-embed-text semantic search) still surfaced relevant chunks, but lost the structured entity linking layer.
Tweet 4/5The pipeline ran entirely on my homelab (Proxmox LXC local Ollama llama.cpp backend). No cloud APIs, no telemetry, no data exfiltration.
Even with the extraction miss, the final grounded response stayed faithful to source material — critical for engineering trust.
Tweet 5/5This is what matters to me in local engineering AI: Don’t corrupt numbers. Don’t lie.
Forge passed the fidelity test where it counts most.
Building reliable offline tools > flashy demos.
#LocalAI #RAG #SelfHosted #Homelab #EngineeringAI
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Jun 11
Can RAG support rapid flood damage assessment without fine-tuning?
Our latest Civil Engineering AI Reading article introduces R2RAG-Flood, a training-free framework that combines retrieval-augmented generation, local historical cases, and reinforced reasoning to estimate post-flood property damage levels.
Key idea:
🔹 Retrieve similar local flood cases
🔹 Use reasoning-guided examples
🔹 Apply conservative severity checks
🔹 Generate interpretable damage assessments
Rather than replacing traditional disaster models, R2RAG-Flood demonstrates how LLMs can provide explainable decision support when rapid assessment is needed and retraining is impractical.
Read more:
cnercrail.com/blog/r2rag-flo…
#AI #LLM #RAG #FloodRisk #DisasterManagement #CivilEngineering #Resilience #EngineeringAI #ClimateRisk
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Jun 11
Physics-based #simulation can't always capture "missing physics." Pure ML lacks robustness. There's a third way. Scientific Machine Learning uses physics where you know it, and neural nets only where there's uncertainty. More accurate, more robust. Michael Hoffmann (30 yrs in simulation-driven development) breaks it down with a real vehicle ride use case in Dyad. Free #webinar —register now - juliahub.com/events/discover…
#Julialang #Dyad #MachineLearning #EngineeringAI #ComputationalEngineering #SciML
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#SCAutomotiveEngineering が #Secondmind AIを活用してエンジニアリング効率を向上
𝙍𝙚𝙖𝙙 𝙈𝙤𝙧𝙚: itbusinesstoday.com/industri…
#Automotive #Cloud #EngineeringAI #SCAutomotiveEngineering #Secondmind
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PhysicsX Raises $300M Series C at $2.4B Valuation
#AI #IndustrialAI #EngineeringAI #EngineeringSimulation #Industry40 #SimulationTech #ManufacturingTech #SeriesC #Funding #PhysicsX
thesaasnews.com/news/physics…
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Nebula Cloud Studio is evolving toward an engineering execution platform for CAD, BIM, spatial intelligence, simulation, and autonomous systems.
#CAD #EngineeringAI #TextToCAD #DigitalEngineering #Manufacturing #Simulation #BIM #SpatialComputing #NebulaCloudStudio
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Apr 10
90% of bad AI CAD outputs are a prompt problem, not a model problem.
Here's how to actually prompt for parametric 3D models 🧵
(built a guide with real do/don't examples)
→ cadxstudio.in
#AI #CAD #EngineeringAI #prompt #mechanicalengineering
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If you could design your ideal AI assistant for engineering… what would it actually do?
Solve complex problems? Explain concepts better? Automate repetitive tasks?
Let’s build this together—what do you really need? 👇
#EngineeringAI #Tech #EngiChat #Innovation
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Some engineering problems don’t need more effort… they need better tools.
AI isn’t here to replace engineers—it’s here to remove friction, speed up thinking, and unlock smarter solutions.
The real advantage? Knowing how to use it.
#EngineeringAI #Innovation #Tech #EngiChat
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Most engineers waste hours switching between tools, searching for answers, and double-checking calculations.
What if one intelligent assistant could handle it all in seconds?
Work smarter, not harder. That’s the real productivity upgrade. ⚙️✨
#EngineeringAI #EngiChat #TechTools
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What’s the one engineering problem that slows you down every single time?
Calculations, debugging, design decisions, or something else?
Let’s talk about it👇 ,you might not be the only one facing it.
#EngineeringProblems #EngiChat #BuildInPublic #EngineeringLife #EngineeringAI
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Some of the most costly engineering mistakes aren’t from lack of knowledge—they’re from missed details. A second set of eyes can be the difference between precision and failure. Smart engineers don’t just design… they verify.
#EngineeringTips #EngineeringAi #EngiChat
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Not every engineering challenge needs hours—some just need the right tool. Imagine having an AI that breaks down complex problems, guides your thinking, and helps you arrive at smarter solutions faster. That’s the edge modern engineers are starting to use. ⚙️
#Engineeringai
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Ever wondered why some structures fail without warning? 🤔
Fatigue failure happens silently over time, even under small repeated loads.
Great engineering isn’t just about strength — it’s about endurance. ⚙️
#EngineeringAI #Engichat
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🤖 AI Agent Implementation 🔗
Tư duy sâu năm 2026: Tại sao Gemini 3 là tiêu chuẩn mới cho Nghiên cứu và Kỹ thuật chuyên nghiệp
Khám phá cách Gemini 3 Deep Think và G…
▶ Đọc thêm
versaroc.co.jp/blog/masterin…
#Gemini3DeepThink #GoogleAIPlus #AIResearchTools #EngineeringAI #VERSAROC
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Mar 5
In this clip, Senior Director of Solutions Engineering Bernardo Mendez shows how Rescale connects simulation work to upstream and downstream systems of record — tying designs, materials, parameters, and requirements to every run and result. This unified trail of context makes it easy to see exactly what was simulated, why, and with which inputs, enabling reliable audits, faster impact analysis when designs change, and confident reuse of past work.
View the full demo on-demand here: rescale.com/lp/rescale-data-…
Stay tuned for the rest of the series!
#DigitalEngineering #SimulationData #EngineeringAI #HPC #DataIntelligence
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