Results 131 to 140 of about 81,631 (193)
This study introduces a data‐driven framework that combines deep reinforcement learning with classical path planning to achieve adaptive microrobot navigation. By training a surrogate neural network to emulate microrobot dynamics, the approach improves learning efficiency, reduces training time, and enables robust real‐time obstacle avoidance in ...
Amar Salehi +3 more
wiley +1 more source
Interactive Tool for Customizing Hydrogel Properties in Practical Applications
This research provides an open‐access tool that enables the scientific community to optimally synthesize hydrogels without requiring expert knowledge, thereby reducing experimental costs. Soft materials represent an interdisciplinary frontier in modern science, combining theoretical and experimental knowledge from diverse fields in both fundamental ...
Ricardo Negrete‐Gallego +5 more
wiley +1 more source
Sharp upper bound for anisotropic Rényi entropy and Heisenberg uncertainty principle. [PDF]
Chatzakou M, Ruzhansky M, Shriwastawa A.
europepmc +1 more source
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
wiley +1 more source
Cure rate estimation with insufficient follow-up: A median-based bootstrap correction approach. [PDF]
Ibi Y, Omori T.
europepmc +1 more source
Human‐Machine Mutual Trust Based Shared Control Framework for Intelligent Vehicles
This work introduces a bidirectional‐trust‐driven shared control framework for human‐machine co‐driving. The method models human‐to‐machine trust from intention discrepancies and Bayesian skill assessment, and machine‐to‐human trust from integrated ability evaluation.
Zhishuai Yin +4 more
wiley +1 more source
A citation index bridging Hirsch's <i>h</i> and Egghe's <i>g</i>. [PDF]
Nuermaimaiti R, Bogachev L, Voss J.
europepmc +1 more source
Enabling Stochastic Dynamic Games for Robotic Swarms
This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley +1 more source
Supervised machine learning models for predicting student mathematics performance in Somaliland primary examinations 2023. [PDF]
Hassan MA +3 more
europepmc +1 more source
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy +2 more
wiley +1 more source

