Researchers create a tool for accurately simulating complex systems
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.
A collaborative research team from the MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.
A new method could provide detailed information about internal structures, voids, and cracks, based solely on data about exterior conditions.
These tunable proteins could be used to create new materials with specific mechanical properties, like toughness or flexibility.
MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.
With further development, the programmable system could be used in a range of applications including gene and cancer therapies.
New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.
J-WAFS researchers are using remote sensing observations to build high-resolution systems to monitor drought.