The research center, sponsored by the DoE’s National Nuclear Security Administration, will advance the simulation of extreme environments, such as those in hypersonic flight and atmospheric reentry.
Popular mechanical engineering course applies machine learning and AI theory to real-world engineering design.
MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.
System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.
VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
New test could help determine if AI systems that make accurate predictions in one area can understand it well enough to apply that ability to a different area.
Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.
A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.
