Modeling and Simulation

Koroush Shirvan, Ben Forget and other MIT researchers show that deep reinforcement learning can be used to design more efficient nuclear reactors.

MIT team has made strides toward a system, which uses physical, analog devices that can much more efficiently mimic the brain’s learning process, for neural network AI systems.

Exotic states called Kohn anomalies could offer clues to why some materials have the electronic properties they do.

Quantum computing will likely be able to tackle the biggest data challenges, but the exact applications may depend on which systems prove most practical. “We’re still in the stage where we’re trying to pick the best technology,” says Paola Cappellaro.

MIT teams to explore novel solutions to reductions in operations and maintenance costs of advanced nuclear reactors Nuclear energy is a low-carbon energy source that is vital to decreasing carbon emissions. A critical factor in its continued viability as a future energy...

The MIT Department of Nuclear Science and Engineering hosted a virtual Graduate Student Expo this year. The annual event showcased posters on groundbreaking research by current NSE doctoral and masters. This year’s virtual Expo provided a unique opportunity for a wider audience...

After delivering novel computational methods for nuclear problems, doctoral candidate plunges into startup life Like the atomic particles he studies, Pablo Ducru seems constantly on the move, vibrating with energy. But if he sometimes appears to be headed in an unexpected direction,...

Fusion energy community makes unified statement on priorities in report for Department of Energy Policy Advisory Group.