Results 141 to 150 of about 691,007 (262)

Knowledge-Fusion-Based Iterative Graph Structure Learning Framework for Implicit Sentiment Identification. [PDF]

Sensors (Basel), 2023
Zhao Y, Mamat M, Aysa A, Ubul K.
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Overharvesting in human patch foraging reflects rational structure learning and adaptive planning. [PDF]

Proc Natl Acad Sci U S A, 2023
Harhen NC, Bornstein AM.
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Structure learning enhances concept formation in synthetic Active Inference agents. [PDF]

PLoS One, 2022
Neacsu V, Mirza MB, Adams RA, Friston KJ.   +3 more
europepmc   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

Differentiating between Bayesian parameter learning and structure learning based on behavioural and pupil measures. [PDF]

PLoS One, 2023
Rutar D, Colizoli O, Selen L, Spieß L, Kwisthout J, Hunnius S.   +5 more
europepmc   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Robust graph structure learning to improve multi-omics cancer subtype classification. [PDF]

BMC Bioinformatics
Guo M, Ye X, Sakurai T.
europepmc   +1 more source

Nonparametric Causal Structure Learning in High Dimensions. [PDF]

Entropy (Basel), 2022
Chakraborty S, Shojaie A.
europepmc   +1 more source