Generating scenarios for extreme events, without extreme data
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
Dimitris Bertsimas and Megan Mitchell discuss the motivation behind Universal Learning, and what sets the new MIT Open Learning educational initiative apart.
Startup accelerator program grows to over 30 companies, almost half of them with MIT pedigrees.
With the help of AI, MIT Research Scientist Judah Cohen is reshaping subseasonal forecasting, with the goal of extending the lead time for predicting impactful weather.
AI supports the clean energy transition as it manages power grid operations, helps plan infrastructure investments, guides development of novel materials, and more.
Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
The new approach “nudges” existing climate simulations closer to future reality.
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
A cross-departmental team is leading efforts to utilize machine learning for increased efficiency in heating and cooling MIT’s buildings.