Engineering Online Learning Resources

What’s hard fun in structural engineering? This semester, my students found out. I introduced parametric modelling into my concrete structures course at Faculty of Civil Engineering CTU in Prague – not only as an extra skill, but as a way to see how structures behave. Using Rhino Grasshopper + Karamba3D, students explored live structural responses to geometric changes – and learned through doing, observing, and yes... debugging. 👷 What did they actually do? 👉 Built a parametric model of a concrete wall 👉 Studied how structural response evolved as they explored various geometric scenarios – from shifting elements to adjusting loads and support conditions 👉 Exported their model into IDEA StatiCa Detail to perform a nonlinear code-check using CSFM 👉 Some even created animations showing how internal forces evolve in real time, or plotted curves illustrating how displacements or maximum stresses change with varying input parameters like column position 🎥 I prepared: 👉 Academic licenses 👉 Full installation & starter guide 👉 11 tutorial videos covering everything step by step 👉 Support for the Rhino → IDEA StatiCa Detail workflow 📊 And the results? Out of 40 students, 31 responded to an anonymous survey: ⭐ Average rating: 8.55 / 10 🔝 90% gave a score of 8 or higher Students appreciated: ✅ Working with modern tools ✅ Visual & interactive understanding of structural behaviour ✅ Logic-based “programmer mindset” ✅ Feeling of relevance to real-world engineering ✅ That it was mandatory – otherwise many wouldn't have dared to try 💬 A few quotes from the feedback: “It was the most fun part of the course – challenging but satisfying.” “At first I was terrified by all those wires… but in the end, I was proud of what I built.” “It wasn’t just following steps – we had to figure out what actually makes the model work.” “I didn’t expect to enjoy it – but it gave me a new way of thinking about structures.” 💡 What I learned: Parametric tools are not just about design freedom – they can transform structural education. They open the door to a new kind of learning: hard fun 🧠✨. 🔍 How about you? Do you use a parametric approach in structural engineering education or practice? 🙏 Thanks go to: Karamba3D, Matthew Tam, Clemens Preisinger – for generously providing academic seats and support Robert McNeel and Associates (TLM, Inc) – for their licensing policy and support for education IDEA StatiCa, Pavel Kaláb, Tomas Oupic Svoboda, Lukáš Juříček – for bringing CSFM into the classroom Lukas Vrablik and prof. Štemberk for the trust and opportunity to try something new in our course Cademy, Aman Agrawal – for teaching me advanced Grasshopper techniques ✨ Sources of inspiration: Krzysztof Wojslaw, Arturo Tedeschi, Arturo De La Fuente, Dr. Milad Showkatbakhsh, Jaroslav Baron, Petr Vacek, Gediminas Kirdeikis, Philippe Block, Peter Debney 🎓 Some of my great thesis pioneers on this topic: Evgenij Bogdanovič, Jan Chmelík, Michal Straka, Durdona Qurbonova

500 students share one computer in Niger. Yet they're conducting advanced physics experiments that students at elite schools can't access. The secret? WebAR turning basic smartphones into portable STEM labs. Think about that. In Sub-Saharan Africa, fewer than 10% of schools have internet. Student-to-computer ratios hit 500:1. Yet mobile subscriptions jumped from single digits to 80% in a decade. Students already carry the infrastructure—we just weren't using it right. Traditional EdTech Reality: ↳ VR headsets: $300+ per student ↳ Heavy apps requiring 5G speeds ↳ Labs costing millions to build ↳ Rural schools: permanently excluded The WebAR Revolution: ↳ Runs in any browser, optimized for 3G ↳ No app store, minimal storage ↳ Science scores improving 10-15% ↳ Every smartphone becomes a laboratory But here's what grabbed me: A physics teacher in rural South Africa has one broken oscilloscope. No budget. Her students scan printed markers, and electromagnetic fields pulse across their desks. They run experiments infinitely—no equipment damaged, no reagents consumed. One student told her: "Engineering is for people like me now. The lab fits in my pocket." What changes everything: ↳ Mobile-first matches actual connectivity ↳ Browser-based works offline ↳ Teachers need training, not new buildings ↳ Inequality becomes irrelevant The Multiplication Effect: 1 teacher with markers = 30 students experimenting 10 schools sharing content = communities transformed 100 districts adopting = educational equality emerging At scale = STEM education without infrastructure gaps We spent decades waiting for labs that won't arrive. Now any browser becomes one. Because when a student in rural Africa explores the same 3D molecules as someone at MIT—using the phone already in their pocket—you realize: WebAR isn't shiny technology. It's a quiet equaliser making world-class STEM education fit into 3G connections and $50 phones. Follow me, Dr. Martha Boeckenfeld for innovations where accessibility drives transformation. ♻️ Share if you believe quality education shouldn't require perfect infrastructure.

Building useful Knowledge Graphs will long be a Humans + AI endeavor. A recent paper lays out how best to implement automation, the specific human roles, and how these are combined. The paper, "From human experts to machines: An LLM supported approach to ontology and knowledge graph construction", provides clear lessons. These include: 🔍 Automate KG construction with targeted human oversight: Use LLMs to automate repetitive tasks like entity extraction and relationship mapping. Human experts should step in at two key points: early, to define scope and competency questions (CQs), and later, to review and fine-tune LLM outputs, focusing on complex areas where LLMs may misinterpret data. Combining automation with human-in-the-loop ensures accuracy while saving time. ❓ Guide ontology development with well-crafted Competency Questions (CQs): CQs define what the Knowledge Graph (KG) must answer, like "What preprocessing techniques were used?" Experts should create CQs to ensure domain relevance, and review LLM-generated CQs for completeness. Once validated, these CQs guide the ontology’s structure, reducing errors in later stages. 🧑⚖️ Use LLMs to evaluate outputs, with humans as quality gatekeepers: LLMs can assess KG accuracy by comparing answers to ground truth data, with humans reviewing outputs that score below a set threshold (e.g., 6/10). This setup allows LLMs to handle initial quality control while humans focus only on edge cases, improving efficiency and ensuring quality. 🌱 Leverage reusable ontologies and refine with human expertise: Start by using pre-built ontologies like PROV-O to structure the KG, then refine it with domain-specific details. Humans should guide this refinement process, ensuring that the KG remains accurate and relevant to the domain’s nuances, particularly in specialized terms and relationships. ⚙️ Optimize prompt engineering with iterative feedback: Prompts for LLMs should be carefully structured, starting simple and iterating based on feedback. Use in-context examples to reduce variability and improve consistency. Human experts should refine these prompts to ensure they lead to accurate entity and relationship extraction, combining automation with expert oversight for best results. These provide solid foundations to optimally applying human and machine capabilities to the very-important task of building robust and useful ontologies.

Here is a comprehensive follow-up post building on our previous updates. This time, a 15-minute video is provided, showing the entire workflow to go from nothing to having a HiL-based Driver in the Loop (DiL) setup. This video covers each step in detail, providing a thorough guide for anyone interested in creating their own simulation environment. #Engineering #DrivingSimulator #Tutorial The video is attached to this post for your convenience. You can also download the models and all files used to generate this example from here: https://lnkd.in/eU6bfYpX The fact that these tools are all (apart from Unreal & Google Earth) part of the MathWorks ecosystem meant that this comprehensive simulator could be made in my free time by utilizing the easy workflows and tool integrations between these tools (#MATLAB, #Simulink, #Simscape, RoadRunner, Speedgoat). As always, stay tuned for more updates, and feel free to reach out if you have any questions or feedback! Video Content Breakdown: - 0:00 - Intro - 0:27 - Google Earth: Used to define the inner and outer limits of the circuit. - 1:05 - MATLAB Live Script: Used to obtain elevation data from online sources, export RoadRunner-compatible files, calculate the minimized curvature optimal racing line, make a lap-time estimation, and export the optimal racing line back to Google Earth. - 3:53 - RoadRunner: Import the generated elevation and road files into RoadRunner, add grass and scenery, trees and buildings, and export to Unreal Datasmith. - 5:15 - Unreal: Open in Unreal, add HUD and minimap, any other blueprints needed, and compile to EXE. - 7:29 - Simscape Vehicle Model & Simulink ECU Model & Unreal-Simulink Integration: Quick walkthrough. - 9:08 - MATLAB Live Script: Used to keep track of high scores and changing settings on the car model, as well as what type of simulation (HiL/MiL) and map to run. - 11:14 - Unreal EXE: Example DiL run on Kyalami. - 12:46 - MATLAB Live Script: Post-processing results and performance. - 13:47 - MATLAB & Unreal: Example of changing vehicle size and tire parameters.

Continuing with the GenAI series, I am excited to share how we revolutionised the knowledge management system (KMS) for a leading client in the manufacturing industry. R&D teams in manufacturing often face the tedious task of manually sifting through complex engineering documents and standard operating procedures to ensure compliance, uphold safety standards, and drive innovation. This manual process is not only time-consuming but also prone to errors. To address this, we collaborated with our client to automate their R&D function’s KMS using Generative AI (GenAI). By allowing precise querying of specific sections of documents, our solution sped up access to critical information, reducing search time from hours to mere seconds. Our Generative AI team processed over 110 R&D-related documents, leveraging Large Language Models (LLMs) to generate accurate responses to complex queries. Hosted on a leading cloud platform with an Angular-based UI, the solution delivered remarkable benefits, including: - Significant accuracy in generated answers - Faster and more accurate data search and summarisation - Enhanced decision-making with easier access to critical R&D information - Improved overall employee productivity By implementing GenAI for knowledge management, the client's R&D function was also able to improve its competitive edge by tracking and responding quickly to market trends and consumer behavior. With plans to scale the solution to process over 1,500 documents across multiple departments, the client is creating a centralised hub for all their information needs. Taking advantage of GenAI can revolutionize knowledge management by delivering the right information to the right person on demand and enabling strategic impact. #GenAI #ManufacturingInnovation #KnowledgeManagement #GenAIseries #GenAIcasestudy #Innovation #R&D #DigitalTransformation #AI #Deloitte

Building your own Python scripts for structural analysis makes sense in two scenarios: 1️⃣ When you need to do a simple analysis and firing up a software programme feels like overkill. 2️⃣ When you need to do a complex analysis and fine control over every aspect of the analysis is critical. Our latest tutorial on EngineeringSkills falls into the first bucket. In this one, we build a script to analyse continuous beams using OpenSeesPy. This is a great starter project for anyone interested in getting started with OpenSeesPy. We parameterise the beam and loading (and include optional hinges) making it a great utility script for future projects. These indeterminate beams are so common that it makes sense to have a script in our toolbox to very quickly generate the shear force diagram, bending moment diagram, reactions and deflection shape. Analysing them by hand takes forever and even setting them up for analysis in commercial software packages can feel laborious. This is where having a simple analysis script comes in handy - quickly plug in the beam parameters, hit Run and you’re done! If you’re a student engineer, having a calculator to check your results against, after each exercise, is a great way of validating your work. It also allows you to generate infinite exercise questions to practice on. 📂 There’s a full video tutorial to go with this tutorial - linked below. You can also download the complete Jupyter Notebook over on the tutorial page on EngineeringSkills. #CivilEngineering #StructuralEngineering #EngineeringSkills #Python #OpenSees #OpenSeesPy #StructuralAnalysis

Sharing a simulation we developed for LearnCheme to help teach undergraduate fluid mechanics. The idea behind these tools is to give students an easy-to-use, interactive platform to visualize concepts that may not be immediately obvious from equations alone. Thanks to National Science Foundation (NSF), IUSE grant for funding. The work is being done at University of Colorado Boulder Chemical and Biological Engineering University of Colorado Boulder College of Engineering & Applied Science. This interactive simulation helps students visualize the differential form of the continuity equation by computing flow rates around a movable control volume. It shows how inflow and outflow balance for divergence-free velocity fields, and how this balance breaks down when the velocity field has nonzero divergence. Link to simulation: https://lnkd.in/g_vKkYTu Other digital simulations for fluids: https://lnkd.in/gHu7GEWY General chemical engineering resources: https://learncheme.com/

We’re hearing from automotive supplier customers that they are trying to run more simulations already during the RFQ response process to respond with a mature engineering concept and avoid later challenges around meeting as-quoted margins or performance characteristics. This becomes especially important when a quote is about a new or complex component. Yet many OEMs expect the response within a few weeks, which doesn’t leave a lot of time for these simulations to be carried out. A traditional desktop simulation tool stack has a hard time coping with this: Simulation expert time, HPC hardware and software licenses become bottlenecks that cause long simulation lead times. That limits the number of design iterations that can be explored and ultimately the response quality. The cloud-native simulation infrastructure of SimScale has scalability built in and hence allows to run as many simulations as needed simultaneously. Simulation experts can ensure analysis quality while they don’t need to actually run the simulations themselves. As a result, more design concepts can be explored faster and response quality increased. If you’re curious, learn how Rimac Technology is leveraging cloud-native simulation to explore broader design spaces in a shorter amount of time via the link in the first comment. And if if you want to experience cloud-native simulation yourself, simply create a SimScale account afterwards and give it a spin! #cloud #simulation #CFD #FEA #thermalmanagement #innovatefaster

Your engineers are brilliant. That's why they keep solving the same problem at different facilities. Over and over. Without knowing someone already figured it out. This isn’t an intelligence problem. It’s an infrastructure problem. Plant A has brilliant engineers.  They found a quality issue costing $8K a month.  Spent three weeks finding the root cause.  Built a smart solution.  Saved $100K a year.  Documented everything.  Problem solved. Six months later, Plant B found the same issue.  Did the same analysis.  Built the same solution.  Saved the same $100K.  Documented it separately.  Problem solved again. Plant C? Also brilliant.  They’re discovering the same issue right now.  Starting the same process.  They’ll solve it soon, for the third time. Same company.  Same brilliance.  Zero knowledge sharing. Each plant keeps its own notes.  No central system.  No easy search like “Has anyone solved this before?”  No alerts when similar problems show up.  No way to turn local wins into company standards. So every plant starts from scratch.  And your best practices stay trapped. Spreadsheets on local drives.  Old email threads.  PowerPoints buried in folders.  Knowledge stuck in people’s heads. Hundreds of great ideas are locked away.  While others waste time reinventing them. That’s lost time, lost money, and lost progress. The best manufacturers treat knowledge like inventory.  You wouldn’t let one plant hoard materials while another runs short.  So why let one plant hoard solutions? When Plant A solves something, it should go into a shared system.  Tagged by equipment, process, and problem.  Searchable for everyone.  Alerting others when similar issues appear.  Scalable across all plants. That’s how local wins become company standards. Plant A’s $100K idea  becomes $300K when shared with B and C.  Same effort.  Triple the impact. In three weeks, all plants could be aligned, instead of six months of duplicate work. Your engineers stop reinventing and start innovating.  New engineers learn faster.  The whole company gets smarter. You already have brilliant engineers.  You already have brilliant solutions.  Now it’s time to multiply that brilliance, not trap it. Because every month knowledge stays isolated,  your competitors move ahead.  They’re solving once and scaling everywhere. Your engineers are brilliant.  Your solutions are excellent.  Your knowledge sharing is broken. Fix the infrastructure,  and brilliance multiplies. P.S. If your best practices are trapped on islands,  let’s talk about building the system that sets them free.  DM me “KNOWLEDGE.”