AI in Aerospace Engineering Applications

Airlines aren’t just talking about AI - they’re already using it to smooth operations, save fuel and keep passengers moving. Delta Air Lines’ Operations Control Centre runs a machine‑learning tool that studies weather patterns and re‑sequences flights hours before storms bite, cutting knock‑on delays. Avionics International easyJet has fitted its entire Airbus fleet with Skywise Predictive Maintenance. Engineers now replace parts before they fail, reducing technical delays and cancellations. Airbus Alaska Airlines dispatchers use Flyways AI to pick the most efficient routes in real time. On long sectors that’s delivering 3‑5 percent fuel and CO₂ savings-over a million gallons a year. Alaska Airlines News PR Newswire Qantas puts personalised fuel‑efficiency analytics in every pilot’s hand via GE’s FlightPulse, driving behaviour changes that trim both fuel burn and emissions. geaerospace.com Lufthansa Systems’ NetLine/Ops ++ aiOCC gives controllers an AI “copilot” that turns masses of live data into recommended actions, helping curb cascading delays across the network. Lufthansa Systems Three take‑aways for carriers still on the fence: AI thrives in the messy middle. It surfaces the next best action when plans unravel. ROI is tangible. Minutes saved, gallons saved, cancellations avoided—every metric lands on the P&L. Humans stay in control. The most successful roll‑outs pair smart algorithms with experienced dispatchers, engineers and pilots. If your airline is still juggling spreadsheets during disruptions, the sky is sending a clear signal: it’s time to bring AI into day‑to‑day ops.

Shield AI just proved AI pilots work. BQM-177A flies high-subsonic autonomously. Integration took weeks, not years. Point Mugu test range. Hivemind AI takes control of a high-subsonic target drone for the first time. No remote pilot. No pre-programmed routes. Pure autonomous decision-making at near-sonic speeds. The technical achievement cuts deep. BQM-177A simulates cruise missiles, with active electronic warfare and maneuvering unpredictably. Hivemind handled it all. Seamless handoff between human operators and AI. Safety protocols intact. Why this matters. Integration timeline. Shield AI went from contract to flight in weeks. Not months. Not years. Weeks. That's the speed standing out against traditional primes. The collaboration tells the story. NAVAIR PMA-281 (strike planning) and PMA-208 (aerial targets) partnered with Kratos Defense. Government reference architecture (A-GRA) compliant. No vendor lock-in. Any platform can integrate Hivemind. Three breakthroughs drive adoption. • Hardware-agnostic design works on any aircraft • GPS-denied operations proven in contested environments   • Human-AI teaming enables safe transition to autonomy Real impact comes from scale. Same month, Hivemind flew on Airbus DT25 and Kratos MQM-178 Firejet. Indian MoD evaluating. Multiple platforms, multiple customers, one AI pilot. Timeline accelerates. More platforms integrating Q4 2025. Operational deployments 2026. When China fields drone swarms, our answer needs autonomous coordination at machine speed. Are your platforms ready for AI integration? Control systems support human-machine handoff? Weeks to integrate means no excuses remain.

✈️ Would you trust engineers working with 1980s-era resolution to design a next-gen aircraft? In simulation, mesh cells are like pixels: the more you have, the more detail you capture. But until now, even advanced engineering teams were limited to coarse meshes—far from the fidelity needed to fully trust or iterate on complex systems. 🛠️ Simulations at industrial scale have long been the bottleneck in designing and operating complex systems—from aircraft to cars to energy infrastructure. For decades, most of the improvement has been coming from Moore's law - CPU speed improvements, rather than software (some of which still use Fortran, an insider admitted during one of my many reference calls...) Enters AI and an amazing team who've been obsessed with that problem for year. So far, most models have broken down beyond meshes with a few 100K cells on multiple GPUs. But yesterday, the Emmi AI team released AB-UPT, the first fluid dynamics model scaling beyond 150 million mesh cells, running on a single GPU, and delivering real-world physics accuracy. - 150 million mesh cells (no typo, that's 1000x more) - Real-world accuracy on a single GPU This is not just faster simulation—it’s AI-native simulation that finally bridges the gap between research demos and industrial-grade engineering. It means aircraft designers can explore concepts in minutes instead of weeks. It means complex systems can be simulated in real time as they operate. At Serena Data Ventures, together with Juliette, Matthieu, Floriane, Bertrand and Charline, we invest in foundational technologies that shift what’s possible in infrastructure and foundational software. Johannes, Dennis, Miks and their whole team are doing just that—and doing it fast. Hats off to all of them for this game-changing breakthrough! 📄 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗽𝗮𝗽𝗲𝗿: https://lnkd.in/deX_zQWx 🤗 𝗧𝗿𝘆 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹: https://lnkd.in/dmd-xtpR 💻 𝗔𝗰𝗰𝗲𝘀𝘀 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲: https://lnkd.in/dZY6EW_P 🧪 𝗖𝗵𝗲𝗰𝗸 𝘁𝗵𝗲 𝗱𝗲𝗺𝗼: https://demo.emmi.ai/ #FoundationalAI #CFD

Check out Maj Christopher Huynh’s insightful space warfighter centric CSET report on leveraging #AI on the Edge of #Space!💫 Key favorite highlights👇 🔷Recent research converges on five SDA functions where Al can relieve bottlenecks: - Catalogue maintenance - Orbit determination - Conjunction assessment - Sensor tasking, - Scheduling and prioritization - Data collection and integration 👉The data collection and integration function presents a challenge because of the diversity of data formats originating from legacy (government-based) systems and new (often commercially based) SDA systems. There is no specifically trained model that can transform telemetry data from different sensors into a common format 🔷Al integration should be prioritized with a focus on: - Enhancing human-machine teaming - Offloading cognitive burden - Accelerating sensemaking - Supporting decision-making for small space operations crews. 🔷Policymakers should consider 1. Invest in the three more mature capabilities: (a) the layered expert-system and neural network for catalog maintenance and uncorrelated-track resolution, (b) the lightweight neural networks for rapid orbit updates and collision-risk triage, (c) the deep reinforcement-learning scheduler for sensor tasking 2. Ensure new systems are built with open interfaces, modular hardware and software components, and ample compute headroom to accommodate emerging Al tools 3. Shift operator culture by launching targeted workshops, on-the-job training programs, and change-management initiatives to demystify new Al capabilities 🔷AI Support to Orbital Warfare - Object and movement detection - Autonomous Guidance, Nav, and Control - Data filtering and prioritization - Autonomous bus subsystem Management - Dynamic comms management - Autonomous payload scheduling and prioritization 🔷Successful onboard compute depends on two key factors: - Selecting radiation-tolerant Al processors - Designing models that operate efficiently within strict power and thermal limits. 🔷Critical policy gaps remain: Current guidance lacks detailed specifications for computational benchmarks, performance metrics, and criteria for test, evaluation, validation, and verification (TEVV) of space-based Al. 🔷Policymakers should consider the following actions: 1. Clearly define the acceptable boundaries of on-orbit autonomy, specifying conditions under which satellites may operate without direct human intervention. 2. Publish detailed technical performance standards, specifying computational benchmarks, allowable power consumption, radiation tolerance, and environmental resilience necessary for effective space-based Al systems. 3. Implement comprehensive, space-specific TEVV processes to ensure Al systems are consistently reliable and trustworthy. 4. Formalize explicit Al acquisition guidelines tailored specifically for satellite systems https://lnkd.in/eTHY2hn9

AI/ML for Engineers – Learning Pathway, Part 2 (Datasets, Code, Projects & Libraries for CAE & Simulation) If you're a mechanical or aerospace engineer diving into ML, you’ve probably realized this: There's no shortage of ML tutorials but very few tailored to simulation, CFD, or physics-based modeling. This second part of Justin Hodges, PhD's blog fills that gap. In the blog, you will find: ➡️ Which datasets actually matter in CAE applications. ➡️ Beginner-friendly vs. advanced datasets for meaningful projects. Links to real engineering data like: ➡️ AhmedML, WindsorML, DrivaerML (31TB of aero simulation data) ➡️ NASA Turbulence Modeling Challenge Cases (with goals for ML-based prediction) ➡️ Johns Hopkins Turbulence Databases ➡️ Stanford CTR DNS datasets, MegaFlow2D, Vreman Research, and more He also points to coding libraries, open-source projects, and suggestions for portfolio-building Especially helpful if you're not publishing papers or attending conferences. Read the full blog here: https://lnkd.in/ggT72HiC Image Source: A Python learning roadmap suggested by Maksym Kalaidov 🇺🇦 in CAE applications! He is a great expert to follow in the space of ML surrogates for engineering simulation. #mechanical #aerospace #automotive #cfd #machinelearning #datascience #ai #ml

AI meets #CFD: 1500+ airfoils, reduced-order models, and deep learning Just discovered a hidden gem for anyone working on aerodynamics, reduced models, or AI-assisted simulation: AI_Airfoil_CFD – an open-source repo that applies CNNs, POD, and FCDNNs to predict aerodynamic performance across 1500+ airfoils from the UIUC dataset. What you’ll find inside: ✅ A full comparison of POD vs. DNN on the Eppler387 airfoil ✅ CNN models trained on UIUC profiles ✅ Preprocessing + visualization scripts ✅ KSCFE course materials and academic references ✅ Perfect for speeding up CFD workflows or building your own digital twin Built by researchers from GIST, this is one of those projects that quietly bridges the gap between simulation engineers and ML engineers. 💡 Repo link: https://lnkd.in/eYmAAug5 #ai #engineering #simulation

Anduril Industries, a defense technology firm known for its use of artificial intelligence in weapons systems, is now setting its sights on the space domain. Fresh off the introduction of next-generation drones and autonomous cruise missiles for the U.S. Air Force, the company announced plans to design, build, and launch its own fully integrated space systems by the end of 2025. “We are expanding our advanced, AI-powered hardware and software capabilities into the final frontier: space,” Gokul Subramanian, Anduril’s senior vice president of space engineering, said Sept. 13 in a news release. The company intends to develop spacecraft for applications like space domain awareness, on-orbit sensor data processing, and satellite defense. The space mission planned for 2025, according to Subramanian, will act as a testbed for Anduril and third-party payloads, with further details to be revealed in the coming months. Anduril’s expansion into space builds on the company’s broader strategy of developing autonomous systems that require minimal human intervention. The company developed a “Collaborative Combat Aircraft,” an autonomous system designed to operate alongside U.S. Air Force piloted aircraft. And it recently announced plans to mass-produce air-breathing, autonomous cruise missiles using commercially available components. Central to Anduril’s technology is its Lattice software, which integrates various sensors and systems for real-time decision-making. Subramanian said Lattice will be used “to autonomously monitor and manage space-based assets, improving situational awareness and reducing operator workload.” The software’s computer vision technology, which processes and interprets visual data from cameras and other imaging systems, allows for real-time object identification and classification, the company said. This technology, crucial for autonomous decision-making in drone operations, could be applied to satellites in orbit so operators on the ground can respond to threats faster. #AI #Space #Satellites

“MIT scientists have developed a deep learning system, Air-Guardian, designed to work in tandem with airplane pilots to enhance flight safety. This artificial intelligence (AI) copilot can detect when a human pilot overlooks a critical situation and intervene to prevent potential incidents. The backbone of Air-Guardian is a novel deep learning system known as Liquid Neural Networks (LNN), developed by the MIT Computer Science and Artificial Intelligence Lab (CSAIL). LNNs have already demonstrated their effectiveness in various fields. Their potential impact is significant, particularly in areas that require compute-efficient and explainable AI systems, where they might be a viable alternative to current popular deep learning models. Air-Guardian employs a unique method to enhance flight safety. It monitors both the human pilot’s attention and the AI’s focus, identifying instances where the two do not align. If the human pilot overlooks a critical aspect, the AI system steps in and takes control of that particular flight element. This human-in-the-loop system is designed to maintain the pilot’s control while allowing the AI to fill in gaps. “The idea is to design systems that can collaborate with humans. In cases when humans face challenges in order to take control of something, the AI can help. And for things that humans are good at, the humans can keep doing it,” said Ramin Hasani, AI scientist at MIT CSAIL and co-author of the Air-Guardian paper. Paper is here: https://lnkd.in/gu_kR-BR https://lnkd.in/gGaRr_kJ