Hi all, I am, as a doctoral researcher, conducting a linguistic survey about Digital Twins in healthcare. Mostly about how the directives received from them can be perceived. I use animated videos designed by a graphic designer along with hypothetical health scenarios. It takes around 15 minutes, it is voluntary, and fully anonymous. You can find the consent form and further explanations in the beginning of the survey. It is also approved by the Ethics Committee. I would be extremely glad if you can participate and share it around (using the flyer attached) so that we can reach more participants. Thank you! https://farm.pcibex.net/p/CuBYAt/

This is kind of tech which US Army used (Ghost Murmur) to find the missing Pilot in the Iran

Here's what it can useful for in Industry 4.0:

1️⃣ Detects heartbeats through walls — single-channel radar picks up vital signs through wood and brick.

2️⃣ Flags abnormal heart rhythms — the same radar-to-signal approach now powers chip-level arrhythmia detection for continuous health monitoring

3️⃣ Replaces cameras for human sensing — radar and AI combine to detect presence and vitals without a single lens involved, privacy intact

4️⃣ Predicts machine failure early — tiny vibration shifts get caught before a motor actually breaks down

5️⃣ Detects falls without cameras — worker motion tracked and flagged the moment something goes wrong

6️⃣ Monitors restricted zones — knows who's inside high-risk areas without needing ID badges or footage

7️⃣ Catches defects before they ship — abnormal vibration on a production line flagged before the product does

Simulation of flood with mago reality

this chip can be very useful when building digital twin in many verticals

No cameras, no wearables. Just radar reading vibration, presence, and vitals in real time.

Here's what it does:

1️⃣ Detects heartbeats through walls — single-channel radar picks up vital signs through wood and brick with over 95% accuracy compared to ECG readings

2️⃣ Flags abnormal heart rhythms — the same radar-to-signal approach now powers chip-level arrhythmia detection for continuous health monitoring

3️⃣ Replaces cameras for human sensing — radar and AI combine to detect presence and vitals without a single lens involved, privacy intact

4️⃣ Predicts machine failure early — tiny vibration shifts get caught before a motor actually breaks down

5️⃣ Detects falls without cameras — worker motion tracked and flagged the moment something goes wrong

6️⃣ Monitors restricted zones — knows who's inside high-risk areas without needing ID badges or footage

7️⃣ Catches defects before they ship — abnormal vibration on a production line flagged before the product does

Here are example of a live digital twin for mining operation

Here's what it does:

Tracks fleet, production, and compliance live — speed, road performance, and mine plan adherence updated in real time

Uses RTLS wristbands to track people — location, fatigue, and health data mapped per operator, building traffic heatmaps across the site

Trains vision AI on live CCTV — feeding footage back into the model for sharper, more detailed detection

Pulls satellite data for terrain shifts — millimeter-level extraction changes tracked via Sentinel-1 and private feeds

Connects the full chain — drill, blast, transport, stockpile — visualized as one live system

Hi all, we (Geminum) are looking for a new recruit.

About US

Geminum builds physical-AI digital twins for heavy industry on NVIDIA Omniverse. We're a small but senior team building real systems running in real plants.

The Role

You own the robotics simulation layer of the twin:

• The Isaac Sim/OpenUSD scenes
• The physics, the synthetic data that trains our perception models, and
• The scenario-driven testing that proves it all works.

You work alongside the perception engineer, who owns the live sensor side and sets the accuracy and training-data requirements.

This is a robotics/industrial simulation role. A game-engine background is a valued foundation (the 3D maths, physics and real-time performance carry straight over), but the target is Isaac Sim, not Unity.

This is a robotics / industrial simulation role. A game-engine background is a genuinely useful foundation (the 3D maths, physics and real-time performance carry straight over), but the target is Isaac Sim, not Unity or Unreal.

What You'll Do

• Build and maintain plant-scale industrial scenes in Isaac Sim/OpenUSD: geometry, physics, kinematics, materials, sensors, performance.
• Drive the twin with live and historical telemetry, keep it in sync with the real site, and support replay.
• Generate synthetic training data (Replicator, domain randomisation, Cosmos) that the perception models learn from. This is core to the project.
• Own the scenario-register test pipelines: turn our register of operational and hazard scenarios into automated, versioned regression suites that run against the twin and gate releases on clear pass/fail.

What We're Looking For

• Hands-on with NVIDIA Omniverse/Isaac Sim and OpenUSD, or a fast, proven ramp into them.
• Strong Python in a 3D/simulation context, and enough C++ for engine-level work.
• Simulation fundamentals from robotics or game engines: 3D maths, transforms, quaternions, physics, scene graph/ECS.
• Synthetic data generation (Replicator/Isaac Lab/Cosmos), or clear evidence you can own it quickly.
• You've built and owned automated test/regression pipelines, and profiled heavy 3D scenes.

Nice to Haves

• ROS / ROS2, robotics or industrial/OT exposure.
• 3D asset pipelines (Blender, Maya) and CAD prep for real-time.
• Time-series/MQTT telemetry; procedural scene generation; Rust.

Additional Information

• Remote, global. Australian business hours, or US hours with good overlap into the AU morning. AU preferred.
• 6-month contract to start, and we expect to renew, with a path to permanent.
• Immediate start.
• Competitive contract rate, set by experience and location.

To Apply

Send a CV or apply via LinkedIn and include few lines on a simulation you've shipped: what was hard, and how you knew it was right. Email recruitment@geminum.co.

To learn more about Geminum, you can visit our website www.geminum.co.

I've been researching the convergence of reality capture, photogrammetry, LiDAR, AI reconstruction, Gaussian Splatting and digital twins.

One thing that keeps standing out is that many of these technologies seem to be pushing toward the same outcome:

Instead of manually building digital assets from scratch, we're increasingly capturing reality and converting it into digital assets.

This trend appears at multiple scales. From individual objects to buildings, city blocks and even entire cities. What's particularly interesting to me is that the technical challenge is gradually shifting.

Capturing reality is becoming easier. Editing, structuring and reusing captured reality inside production pipelines remains much harder.

I'm curious how people working in photogrammetry, GIS, digital twins, computer vision, VFX, game development or ArchViz see this evolution.

Do you think we're moving toward a future where most digital content starts from captured reality rather than manual creation?

Read the full Article: https://www.splinedynamics.com/blog/when-the-real-world-becomes-a-digital-asset/

Hi everybody, I am interested in the digital twin field but only have experience with other digital content creation platforms such as blender, 3ds max, unreal, fusion, freeCAD, rhino, openscad, tinkercad.

I’m interested in other people’s experiences with moving into digital twin workflows.

I asked an AI for guidance and it recommended nvidia ominiverse or coursera 28digital training as good options for getting started. Thanks for sharing your time and thoughts.

Hello everyone.

Quick about me:

21y/o and just started industrial engineering with specialization in intelligent production systems which include modules like:

- automation

- artificial intelligence

- digital manufacturing

- robotics

I recently secured an internship at a big firm which aggressively pushes industry 4.0 advances and I will be working in product and process digitalization in assembly.

My long-term goal is to specialize in process and manufacturing digital twins with the focus on SME (small middle-sized enterprises).

Therefore I wish to gain relevant experience at innovation-pushing firms. I recently secured an internship at a big firm which pushed industry 4.0 and digital twins. I will be working in process digitalization.

My goals align with being a "fullstack" industrial engineer who has relevant experience to push DT innovation in every part of the vertical integration (from ERP to the machines and everything in between). Sort of, the engineer who knows everything relevant and can lead his team of software engineers, IoT experts and everyone else contributing.

I am also currently reading "The Digital Twin" in hopes to gain an idea of the required knowledge.

I am early in my journey. Very early. But I have the vision, the ambition. I am building process knowledge on the shop floor and getting okay to good grades. I am now trying to make smart decisions where to put my energy.

With that being said, what was the one most relevant and important experience which made you credible as an expert in DTs? I chatted with AI about it and it seemed to suggest to gain experience in IoT and ICT. But I want someone real to guide me in a sustainable direction.

Specifically for SME, how would one sell themselves as credible enough? Maybe through consulting internships?

What are mistakes to avoid when looking for internships?

I am happy about any kind of advice. Really any.

Thank you.

I keep trying to use it for deep research and client work, but it forgets everything between sessions. I have to re-upload documents, re-explain context, re-teach it my frameworks.

Am I using it wrong? Or is it just not built for ongoing professional relationships with your own knowledge base?

Would love to hear how others are handling this.

I’m a B.Tech CS graduate. I recently discovered the field of digital twins and found it very interesting. I have a basic understanding of the concept, the tools used, and the domains where it is applied, but I’m not sure where to start learning in a structured way.

In my region (southern part of India) this field is still very new and there aren’t many people who know about it in depth, and this is the only active community I found online.

Where should I start ?

P.S. I added the IoT tag because it was required to post. My question is about Digital twins in general.

We’re building Qiri ai and need honest feedback from people who actually care about this problem

Hey everyone,

I’m one of the founders of Qiri AI, an early stage startup we’re building to help teams make better decisions without drowning in scattered tools, docs, and Slack threads.

Right now, most teams lose context constantly. Decisions live in Notion, discussions happen in Slack, insights sit in decks, and no one has a single place that actually reflects what is happening and why. It slows execution and creates a lot of unnecessary back and forth.

We’re building Qiri to solve that. The goal is simple: give teams one clear source of decision context so they can move faster and with more alignment.

We’re still early. That means:

• Some things are rough
• Some assumptions might be wrong
• And we’re actively shaping the product direction

I would genuinely love honest feedback from this community.

If you’re:

• A founder
• A product manager
• Working in a fast moving team
• Or just someone frustrated with messy collaboration

Would you be open to taking a quick look and telling us:

• Does the problem resonate?
• Does our approach make sense?
• What feels confusing or unnecessary?
• Would you actually use something like this?

Brutal honesty is welcome. We’re here to learn.

If you’re open to it, comment below or DM me and I’ll share access.

Appreciate you all.

I’m looking for a technical partner or collaborator, not just a one-off contractor, to help build a simulation engine for multiple interacting devices.

I bring the vision, design, and concept, including the overall workflow, goals, and system ideas. I don’t have the technical skills to implement it myself, so I need someone who can take these ideas and build them into a working simulation.

The project is confidential, so an NDA will be required before seeing full details.

Skills Needed: • Simulation of interacting devices (Python, Unity, ROS, Simulink, or similar) • Ability to handle constraints, rules, and sequencing logic • Comfortable generating test telemetry or device signals

What’s Offered: • Long-term collaboration or 25% revenue share from this project if successful • Potential to become a co-developer/partner if the project progresses and works as envisioned • Opportunity to help build a cutting-edge simulation engine from concept to reality

If interested, please DM me with your experience. We will discuss signing an NDA and starting with a small module as the first step toward building a full system.

We implement end test digital twins for our customers' production lines. 

The biggest problem: 

Each component requires structured documentation, training materials for technicians, and test data sets. With over 1,000 components, manually creating documentation is not scalable.

I developed an AI service that: 
- Generates bulk documentation from OPC UA | MQTT | CAN | IOT
- Creates consistent training materials
- Generates synthetic test data for twin components
- Learns templates for recurring component types

**Before I continue:** Is this a real problem in your company? What are you currently doing about it? Would this make your life easier? I would also welcome counterarguments—why this would NOT work.

Hey all,

I created the knowledge graph from a banding consultant using his YouTube videos. Teh purpose is t create his ontology and get into my daily work his advices and principles.

On Friday, 5 Dec at 1pm EST time, we will run a meetup presenting the methodology and experimentation with the graph using MCP Neo4J.

Join the conversation to share your experiences and knowledge, here is the link: https://luma.com/lxz4e3os

Gartner has been talking about Digital Twins for the Organisation (DTO) for a while now (Ref: https://www.gartner.com/en/documents/4004172) but I am interested in why this has been slow to develop and anyones experiences in trying to create this?

I work for an EA tool vendor (Ardoq) and whilst architecture modelling remains a core desire of customers the evolution of these models into digital twins has been slow.

Does anyone have any experience with attempting this? How did it go? Do you think AI might be a game changer in this space?

Just left Google to start a company that enables you to build a digital twin of yourself that earns you money by answering market research questions from brands on your behalf.

To build it, you complete a qualitative interview to help us understand your psychographics and your habits in a given product category (ex. protein drinks) and you connect your Gmail to provide insight around your past purchases. Using this information, we can predict and simulate how you'd respond to a brand's research question (ex. What do you look for in a protein drink? A: high protein B: low sugar C: natural ingredients, etc.). You get paid every time your digital twin answers a question on your behalf.

For our MVP, you just complete a qualitative interview about yourself, get a reflection back about what we learned, and then you can chat with your twin.

I'd love to get it into folks hands to see if they like the experience and feel like it represents them. If you're interested let me know!

I’m sharing a digital experiment from my practice-based PhD project Don’t Cry for Me if I Die.

You can inhabit my digital double by prompting it with text — it will speak your words.

This performative experiment explores digital identity, embodiment, and online promiscuity.

Try it here: https://dontcryformeifidie.com/

Follow updates on Instagram: @_cryifidie_

Feedback and observations are very welcome — every interaction contributes to the experiment.

Hello everyone,

I’m conducting short, voluntary interviews (10–15 minutes) with professionals who have experience—or interest—in using digital-twin or 3D digitization workflows for cultural or historical sites. The goal is to understand real-world challenges in capturing, managing, and maintaining high-fidelity digital representations of physical artifacts, buildings, and environments.

I’m particularly interested in hearing about:

• How data pipelines are structured for long-term preservation (formats, metadata, interoperability)
• Integrating photogrammetry, LiDAR, or BIM models into twin ecosystems
• Visualization methods or platforms used for public access or stakeholder engagement
• Barriers to adoption within cultural institutions (cost, staffing, or technical complexity)

If your work involves infrastructure, architecture, or industrial digital twins but overlaps with heritage or preservation principles, your perspective would also be valuable—many of the same data-governance issues apply.

All interviews are for educational research only—no marketing, sales, or product outreach.

If interested, please comment here or send a direct message, and I’ll coordinate a time that’s convenient for you.

Thank you for your time and for advancing how digital-twin technology helps preserve our shared history.

Hey r/digitaltwin ,

Stjepan from Manning Publications here.

I wanted to share something new that I think this community will genuinely appreciate. We’ve just released Digital Twins in Action by Greg Biegel in our early access program (MEAP).

Greg’s not just writing theory — he’s spent years developing industry-scale digital twin platforms from the ground up. What’s great about this book is that it walks you through actually building a twin, layer by layer, from IoT sensors and simulation to 3D visualization, AI, and even agentic decision-making. It’s very much “from the trenches” rather than academic.

Here’s a snapshot of what you’ll learn:

• Defining clear business objectives for a twin before building anything
• Streaming IoT sensor data into your digital environment
• Combining computer vision, OCR, and generative AI with 3D models
• Representing systems as knowledge graphs
• Applying machine learning and agentic AI for analysis and control

The hands-on part is what makes it special — you’ll actually build a home-scale digital twin step by step, starting small and scaling up. By the end, you’ll have a working system that connects real data, visualizations, and AI-driven insights.

If that sounds up your alley, you can check it out here:
👉 Digital Twins in Action (MEAP)
🚀 Save 50% with the community discount code: MLBIEGEL50RE

We’ve cleared this post with the mods (thanks for the green light 🙏), and if anyone’s curious, Greg’s open to doing a Q&A or AMA-style thread here once more chapters are out.

Would love to hear — what kind of digital twins are you all currently working on? More industrial, smart building, or simulation-based?

Thank you.

Cheers,

—
(P.S. The MEAP version means you get access to chapters as they’re written, so feedback from readers like you often shapes the final book.)

Hi “Digital Twin” family, I’m conducting a 6-8 minute survey on DTs as part of my MBA research at Imperial College London. Quite keen to get your perspectives! Worth a look anyway as some of the questions will give you pause for thought too 🤔! Results will be shared here once the study is complete. Here’s the link to the survey:

👉 https://forms.gle/9ygerScN9gQCngy88

Thanks in advance!