Results 141 to 150 of about 816,227 (284)

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Addendum: Implicit learning of temporal behavior in complex dynamic environments. [PDF]

Psychon Bull Rev, 2022
Salet JM, Schlichting N, Kruijne W, van Rijn H.   +3 more
europepmc   +1 more source

Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Advanced Intelligent Discovery, EarlyView.
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, Tong Wu, Baian Chen, Zikang Li, Qiuyang Lu, Lu Lu, Hon Ho Wong, Cheuk Hei Chan, Bolong Huang   +8 more
wiley   +1 more source

ChatCFD: A Large Language Model‐Driven Agent for End‐to‐End Computational Fluid Dynamics Automation with Structured Knowledge and Reasoning

Advanced Intelligent Discovery, EarlyView.
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, Kang Hu, Zhuowen Wu, Jiangyang Ge, Jiawei Miao, Yuzhi Zhang, He Sun, Weizong Wang, Tianhan Zhang   +8 more
wiley   +1 more source

Selecting for Learning Potential: Is Implicit Learning the New Cognitive Ability? [PDF]

J Intell, 2022
Montuori LM, Montefiori L.
europepmc   +1 more source

Wearable Metamaterials with Embodied Intelligence for Programmable Control of Human Limbs Tremor

Advanced Intelligent Systems, EarlyView.
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, Marcela R. Machado, Tanmoy Mukhopadhyay   +2 more
wiley   +1 more source

Selective engram coreactivation in idling brain inspires implicit learning. [PDF]

Proc Natl Acad Sci U S A, 2022
Aly MH, Abdou K, Okubo-Suzuki R, Nomoto M, Inokuchi K.   +4 more
europepmc   +1 more source

IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Advanced Intelligent Systems, EarlyView.
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, Olatunji Omisore, Wenjing Du, Wenke Duan, Chen Bailang, Liu Kun, Lei Wang, Minxin Wei   +7 more
wiley   +1 more source

Implicit Learning of True and False Belief Sequences. [PDF]

Front Psychol, 2021
Ma Q, Heleven E, Funghi G, Pu M, Baetens K, Deroost N, Van Overwalle F.   +6 more
europepmc   +1 more source

Multiobjective Environmental Cleanup with Autonomous Surface Vehicle Fleets Using Multitask Multiagent Deep Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
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, Samuel Yanes, Manuel Perales, Daniel Gutiérrez, Sergio Toral   +4 more
wiley   +1 more source