Results 141 to 150 of about 1,145,514 (294)
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun +8 more
wiley +1 more source
Chat computational fluid dynamics (CFD) introduces an large language model (LLM)‐driven agent that automates OpenFOAM simulations end‐to‐end, attaining 82.1% execution success and 68.12% physical fidelity across 315 benchmarks—far surpassing prior systems.
E Fan +8 more
wiley +1 more source
Wearable Metamaterials with Embodied Intelligence for Programmable Control of Human Limbs Tremor
Resulting from alternating muscle contractions, tremors can severely limit human ability to perform everyday tasks like walking or talking, due to their disruptive nature. Medication and surgery may not always effectively address tremor control. A wearable device embodying programmable smart metamaterials with adaptable intelligence to meet the demand ...
Braion Barbosa de Moura +2 more
wiley +1 more source
Crater Observing Bioinspired Rolling Articulator (COBRA)
Crater Observing Bio‐inspired Rolling Articulator (COBRA) is a modular, snake‐inspired robot that addresses the mobility challenges of extraterrestrial exploration sites such as Shackleton Crater. Incorporating snake‐like gaits and tumbling locomotion, COBRA navigates both uneven surfaces and steep crater walls.
Adarsh Salagame +4 more
wiley +1 more source
IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi +7 more
wiley +1 more source
This study presents a multitask strategy for plastic cleanup with autonomous surface vehicles, combining exploration and cleaning phases. A two‐headed Deep Q‐Network shared by all agents is traineded via multiobjective reinforcement learning, producing a Pareto front of trade‐offs.
Dame Seck +4 more
wiley +1 more source
Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian +37 more
wiley +1 more source
Elastic Fast Marching Learning from Demonstration
This article presents Elastic Fast Marching Learning (EFML), a novel approach for learning from demonstration that combines velocity‐based planning with elastic optimization. EFML enables smooth, precise, and adaptable robot trajectories in both position and orientation spaces.
Adrian Prados +3 more
wiley +1 more source
A hierarchical multimodal framework coupling a large language model for task decomposition and semantic mapping with a fine‐tuned vision‐language model for semantic perception, enhanced by GridMask, is presented. An aerial‐ground robot team exploits the semantic map for global and local planning.
Haokun Liu +6 more
wiley +1 more source
Soft Robotic Sim2Real via Conditional Flow Matching
A new framework based on conditional flow matching addresses the persistent Sim2Real gap in soft robotics. By learning a conditional probability path, the model directly transforms inaccurate simulation data to match physical reality, successfully capturing complex phenomena like hysteresis.
Ge Shi +6 more
wiley +1 more source

