Model predicts long-term effects of nuclear waste on underground disposal systems
The simulations matched results from an underground lab experiment in Switzerland, suggesting modeling could be used to validate the safety of nuclear disposal sites.
The simulations matched results from an underground lab experiment in Switzerland, suggesting modeling could be used to validate the safety of nuclear disposal sites.
TactStyle, a system developed by CSAIL researchers, uses image prompts to replicate both the visual appearance and tactile properties of 3D models.
Materials scientist is honored for his academic leadership and innovative research that bridge engineering and nature.
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.
The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.