A new approach leverages discrete representations of protein structure to efficiently generate diverse and realistic conformational ensembles, offering a fresh perspective on modeling protein dynamics.
New research reveals occupant behavior during demand response events is driven by factors extending beyond simple thermal preferences.
Researchers have developed a new framework to reliably train and evaluate AI agents’ ability to use tools without the limitations of real-world API access.
A new approach leverages the power of artificial intelligence to automatically derive accurate and understandable models of static friction for robotic systems.
Researchers have developed a deep learning framework capable of accurately modeling diverse physical systems governed by different partial differential equations.
New research demonstrates a method for rapidly teaching robots diverse manipulation skills with minimal training data.
A new system harnesses the power of artificial intelligence to build and execute complex network measurement studies, lowering the barriers to in-depth internet analysis.
Researchers have developed an untethered, shape-morphing robot capable of seamlessly transitioning between land, underwater, and surface locomotion with improved energy efficiency.
Researchers have developed a reinforcement learning system that allows bipedal robots to execute controlled falls, prioritizing both safety and aesthetic pose achievement.
A new unsupervised approach uses artificial intelligence to analyze stellar spectra and reveal the chemical composition of stars without relying on pre-labeled data.