MIT engineers’ algorithm may have wide impact, from forecasting climate to projecting population growth to designing efficient aircraft.

Collaborative multi-university team will pursue new AI-enhanced design tools and high-throughput testing methods for next-generation turbomachinery.

A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.

Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.

Ammonia could be a nearly carbon-free maritime fuel, but without new emissions regulations, its impact on air quality could significantly impact human health.

The new design approach could be used to produce metals with exceptional combinations of strength and ductility, for aerospace and other applications.

During the MIT Science Policy Initiative’s Congressional Visit Days, PhD students and postdocs met with legislators to share expertise and advocate for science agency funding.

MIT CSAIL’s frugal deep-learning model infers the hidden physical properties of objects, then adapts to find the most stable grasps for robots in unstructured environments like homes and fulfillment centers.

With generative AI models, researchers combined robotics data from different sources to help robots learn better.

A new MIT system could help astronauts conserve energy and extend missions on the lunar surface.