Results 111 to 120 of about 8,038,825 (297)

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data

Advanced Engineering Materials, EarlyView.
Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour, Prithwish Tarafder, Anton Wiberg, Huanyu Li, Johan Moverare, Patrick Lambrix   +5 more
wiley   +1 more source

Unsupervised Structural Graph Node Representation Learning [PDF]

, 2020
Unsupervised Graph Representation Learning methods learn a numerical representation of the nodes in a graph. The generated representations encode meaningful information about the nodes\u27 properties, making them a powerful tool for tasks in many areas ...
Joaristi, Mikel
core  

Graph Representation Learning with Deep Recurrent Models [PDF]

, 2019
Representing and comparing graphs is a central problem in many fields. We propose sequence- to-sequence architectures for graph representation learning in both static and dynamic settings.
Aynaz Taheri (9907230)
core   +1 more source

Learning Graph Representations with Embedding Propagation

CoRR, 2017
We propose Embedding Propagation (EP), an unsupervised learning framework for graph-structured data. EP learns vector representations of graphs by passing two types of messages between neighboring nodes. Forward messages consist of label representations such as representations of words and other attributes associated with the nodes.
Alberto García-Durán, Mathias Niepert
openaire   +4 more sources

Fostering Innovation: Streamlining Magnetocaloric Materials Research by Digitalization

Advanced Engineering Materials, EarlyView.
Magnetocaloric cooling (MCE) is an environmentally friendly refrigeration method with great potential. Optimizing MCE materials involves the preparation and screening of large quantities of samples, which in turn generates a large amount of data. A digitalization approach is presented that uses ontologies, knowledge graphs, and digital workflows to ...
Simon Bekemeier, Moritz Blum, Luana Caron, Alisa Chirkova, Philipp Cimiano, Basil Ell, Inga Ennen, Michael Feige, Maik Gaerner, Thomas Hilbig, Andreas Hütten, Günter Reiss, Tapas Samanta, Sonja Schöning, Christian Schröder, Lennart Schwan, Chris Taake, Martin Wortmann   +17 more
wiley   +1 more source

GCL-ALG: graph contrastive learning with adaptive learnable view generators [PDF]

PeerJ Computer Science
Data augmentation is a pivotal part of graph contrastive learning, which can mine implicit graph data information to improve the quality of representation learning.
Yafang Li, Jie Kang, Zhihua Chu, Baokai Zu   +3 more
doaj   +2 more sources

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Graph latent diffusion-based molecular representation learning for enhanced generalization in molecular property prediction

Journal of Cheminformatics
This study aims to evaluate the effect of latent diffusion models on molecular representation learning from the perspective of generalization performance in molecular property prediction.
Daiki Koge, Naoaki Ono, Takashi Abe, Shigehiko Kanaya   +3 more
doaj   +1 more source

The PRIMA Thesaurus for Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
The PRIMA Thesaurus is a structured vocabulary designed to improve how materials science data is described and shared. Developed with input from multiple experts, it enables clear documentation of research workflows, data exchange, and reuse across platforms.
Rossella Aversa, Alexey Boubnov, Dario De Angelis, Catriona Eschke, Maximilian Kabbe, Felix Kraus, Mirco Panighel, Richard Thelen, Davide Valentinis   +8 more
wiley   +1 more source