# Mechanical engineering Published articles for Mechanical engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications DevFeed: [MIT researchers develop a generative AI method for enforcing hard constraints in safety-critical applications]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Generative AI](), [AI Models](), [Algorithm](), [Requirements](), [Computer vision](), [Robotics]() Tags: [ai](), [ai-models](), [algorithm](), [algorithms](), [artificial-intelligence](), [computer-science-and-technology](), [computer-vision](), [diffusion-models](), [electrical-engineering-and-computer-science-eecs](), [flow-matching](), [generative-ai](), [hard-constrained-sampling](), [hardflow](), [idss](), [kaveh-alim](), [laboratory-for-information-and-decision-systems-lids](), [machine-learning](), [mechanical-engineering](), [mit-schwarzman-college-of-computing](), [navid-azizan](), [optimal-control](), [paper](), [requirements](), [research](), [robotics](), [safe-ai](), [school-of-engineering](), [trajectory-optimization](), [zeyang-li]() ### AI overview MIT researchers developed a deployment-time technique that lets pretrained generative AI models explore solutions while enforcing hard constraints on final outputs. Experiments in robotics, physical-process control, and computer vision found that the method satisfied required constraints and identified better solutions than existing techniques. ### Source excerpt The "HardFlow" algorithm could help generative AI models produce high-quality outputs that obey strict requirements when "pretty close" doesn't cut it. ## MIT spinout turns plastic waste into resilient building materials DevFeed: [MIT spinout turns plastic waste into resilient building materials]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-09-14T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Artificial Intelligence](), [systems]() Tags: [3-d-printing](), [ai](), [aj-perez](), [alumni-ae](), [artificial-intelligence](), [atlas-composites](), [cleaner-industry](), [engineering](), [homes](), [housing](), [manufacturing](), [materials-science-and-engineering](), [matt-pouliot](), [mechanical-engineering](), [platform](), [pollution](), [production](), [recycled-plastic-building-materials](), [robotics](), [school-of-engineering](), [startups](), [sustainability](), [u-s-army](), [water]() ### AI overview MIT spinout Atlas Building Composites is commercializing an AI-powered robotic manufacturing platform that recycles single-use and low-grade plastic into durable building components. Its waterless process has been used for structures including a bridge supplied to the U.S. Army Corps of Engineers. ### Source excerpt Atlas Building Composites is commercializing MIT research to turn plastic waste into parts for buildings and other infrastructure. ## Generating scenarios for extreme events, without extreme data DevFeed: [Generating scenarios for extreme events, without extreme data]() Original publisher: [Read original article]() Author: Jennifer Chu | MIT News Published: 2026-08-24T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Machine learning](), [Algorithms](), [Critical Infrastructure](), [data]() Tags: [algorithm](), [algorithms](), [artificial-intelligence](), [center-for-computational-science-and-engineering](), [climate](), [climate-risk-assessment](), [computer-modeling](), [critical-infrastructure](), [data](), [extreme-event-aware](), [extreme-weather](), [fire](), [heat](), [idss](), [kai-chang](), [learning-fefb62e9fa83](), [machine-learning](), [mechanical-engineering](), [mit-meche](), [mit-schwarzman-college-of-computing](), [natural-disasters](), [research](), [risk](), [school-of-engineering](), [storm](), [sustainability](), [themis-sapsis](), [weather](), [weather-prediction]() ### AI overview MIT engineers developed a machine-learning algorithm that generates plausible future extreme-event scenarios without requiring past extreme events in the training data. It learns from available records, filters out implausible weather scenarios, and estimates events' frequency, size, intensity, duration, and area of impact to help planners prepare. ### Source excerpt A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for. ## A better way to turn 2D designs into 3D models for rapid prototyping DevFeed: [A better way to turn 2D designs into 3D models for rapid prototyping]() Original publisher: [Read original article]() Author: Adam Zewe | MIT News Published: 2026-07-16T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [3D](), [vlm](), [Artificial Intelligence](), [AI Development](), [Computing](), [coding]() Tags: [3d](), [ai](), [ai-models](), [algorithms](), [artificial-intelligence](), [automated](), [computer-aided-design-cad](), [computer-modeling](), [computer-science-and-technology](), [computing](), [design](), [efficiently](), [faez-ahmed](), [generation](), [generative-ai](), [geometric-inference-feedback-tuning-gift](), [giorgio-giannone](), [machine-learning](), [mechanical-engineering](), [mit-ibm-computing-research-lab](), [mit-schwarzman-college-of-computing](), [performance](), [rapid-prototyping](), [research](), [school-of-engineering](), [systems-design](), [vision-language-models-vlms]() ### AI overview Researchers developed an automated framework that teaches vision-language models to convert 2D designs into more accurate and functional CAD programs while using less computation. The system turns model failures into training data to improve CAD generation and support rapid prototyping. ### Source excerpt Researchers developed an automated framework that helps AI models generate CAD programs more accurately and efficiently. ## MIT's JARVIS Challenge tests AI copilots in jet-engine design and manufacturing DevFeed: [MIT's JARVIS Challenge tests AI copilots in jet-engine design and manufacturing]() Original publisher: [Read original article]() Author: Department of Aeronautics and Astronautics Published: 2026-07-14T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Development](), [Artificial Intelligence](), [Hardware](), [Software Engineering](), [Large language models (LLMs)]() Tags: [3-d-printing](), [aeronautical-and-astronautical-engineering](), [aerospace](), [ai-and-rapid-prototyping](), [ai-copilots](), [ai-native-engineer](), [aircraft](), [andreea-bobu](), [artificial-intelligence](), [chatgpt](), [classes-and-programs](), [claude](), [contests-and-academic-competitions](), [design](), [design-build-test-cycle](), [education-teaching-academics](), [engineering](), [gas-turbine-aero-engine](), [generative-ai](), [hardware](), [independent-activities-period](), [jet-engines](), [large-language-models-llms](), [lincoln-laboratory](), [logistics](), [machine-learning](), [manufacturing](), [masha-folk](), [mechanical-engineering](), [mit-aeroastro](), [mit-gas-turbine-laboratory](), [mit-iap](), [mit-jarvis-challenge](), [mit-lincoln-laboratory](), [mit-meche](), [mit-motorsports-team](), [mit-parley](), [mit-rocket-team](), [robotics](), [school-of-engineering](), [stem-education](), [students](), [undergraduate](), [zachary-cordero](), [zoltan-spakovszky]() ### AI overview MIT's JARVIS Challenge asked undergraduate teams to design, fabricate, assemble, and test small gas turbine aero engines with AI as their primary engineering partner. The challenge found that AI could accelerate parts of safety-critical hardware engineering, while engineering judgment remained essential and manufacturing was the main rate-limiting step. ### Source excerpt MIT students designed, built, and tested a jet engine with AI copilots, assessing AI's usefulness in developing high-performance aerospace systems.