# Computer modeling Published articles for Computer modeling. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## Paving the way for greener ammonia production DevFeed: [Paving the way for greener ammonia production]() Original publisher: [Read original article]() Author: David L. Chandler | Department of Materials Science and Engineering Published: 2026-08-20T18:45:00Z Content type: article Language: en Sources: [MIT AI News]() Topics: [Materials science and engineering](), [acid]() Tags: [agriculture](), [ai-for-materials-science](), [artificial-intelligence](), [bilge-yildiz](), [catalysts](), [chemistry](), [cleaner-fertilizer](), [cleaner-industry](), [computational-materials-science](), [computer-modeling](), [dmse](), [electrochemical-ammonia-production](), [emissions](), [energy](), [fertilizer-production](), [food](), [fossil-fuel](), [green-ammonia](), [greener-fertilizer](), [haber-bosch-process](), [industry](), [machine-learning](), [materials-science-and-engineering](), [mit-dmse](), [nitrogen-dissociation](), [nuclear-science-and-engineering](), [pollution](), [research](), [school-of-engineering](), [school-of-science](), [science](), [sustainability](), [transition-metal-nitrides]() ### AI overview MIT researchers developed an approach to predict promising catalyst materials for electrochemical ammonia production. The method could speed the search for alloys that may help make this lower-emissions process more competitive with the fossil-fuel-dependent Haber-Bosch process. ### Source excerpt New MIT research could lead to better materials for a fossil-fuel-free process for making the chemical that's essential to fertilizer and other products. ## GeoPT helps AI models simulate how objects respond to physical forces DevFeed: [GeoPT helps AI models simulate how objects respond to physical forces]() Original publisher: [Read original article]() Author: Alex Shipps | MIT CSAIL Published: 2026-08-10T19:25:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [AI Models](), [Simulation](), [AI Research](), [Computer Science and Artificial Intelligence Laboratory (CSAIL)](), [Robotics]() Tags: [3-d-imaging](), [ai](), [ai-models](), [artificial-intelligence](), [computational-fluid-dynamics-cfd](), [computer-graphics](), [computer-modeling](), [computer-science-and-artificial-intelligence-laboratory-csail](), [computer-science-and-technology](), [crash-simulation](), [design](), [electrical-engineering-and-computer-science-eecs](), [geometric-pre-training](), [geopt](), [haixu-wu](), [human-computer-interaction](), [kaiming-he](), [manufacturing](), [minghao-guo](), [mit-csail](), [mit-eecs](), [mit-schwarzman-college-of-computing](), [neural-physics-simulation](), [paper](), [physics](), [physics-aware-ai](), [physics-foundation-models](), [research](), [robotics](), [school-of-engineering](), [self-supervised-learning](), [simulation](), [surrogate-modeling](), [synthetic-dynamics](), [transformer-based-simulators](), [wojciech-matusik]() ### AI overview Researchers at MIT CSAIL and Tsinghua University developed GeoPT, a pre-training approach that uses 3D simulations of mechanical interactions to help AI models learn physics more efficiently. The article reports that models using the approach reached peak performance twice as fast and trained on up to 60 percent less data than leading models. ### Source excerpt "GeoPT" helps AI models understand the basics of physics so they can simulate how objects respond to things like wind and water more efficiently and accurately. ## 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. ## A better way to model the behavior of metal alloys DevFeed: [A better way to model the behavior of metal alloys]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-06-19T18:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [Simulation](), [Machine learning](), [datasets]() Tags: [artificial-intelligence](), [chemical-engineering](), [chemistry](), [computer-modeling](), [computer-science-and-technology](), [daniel-xiao](), [datasets](), [dmse](), [killian-sheriff](), [lewis-r-owen](), [machine-learning](), [materials-science-and-engineering](), [mit-materials-science-and-engineering](), [models](), [phase-diagrams](), [predicting-new-materials](), [predictions](), [research](), [rodrigo-freitas](), [school-of-engineering](), [simulation](), [yifan-cao]() ### AI overview MIT researchers developed a machine-learning approach that uses diverse training datasets to model chemically complex metal alloys and predict their material properties more accurately across different conditions. ### Source excerpt MIT researchers' approach captures subtle atomic patterns, improving predictions of material properties.