Results 41 to 50 of about 48,282 (262)

What Do Large Language Models Know About Materials?

open access: yesAdvanced 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   +2 more
wiley   +1 more source

Evaluating unsupervised fault detection in self-healing systems using stochastic primitives [PDF]

open access: yes, 2015
This research was partially supported by the Scottish Informatics and Computer Science Alliance (SICSA).Autonomous fault detection represents one approach for reducing operational costs in large-scale computing environments.
Barker, Adam David   +5 more
core   +1 more source

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

open access: yesAdvanced 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   +6 more
wiley   +1 more source

Value at risk and self-similarity [PDF]

open access: yes, 2007
The concept of Value at Risk measures the "risk" of a portfolio and is a statement of the following form: With probability q the potential loss will not exceed the Value at Risk figure. It is in widespread use within the banking industry.
Olaf Menkens, Menkens, Olaf
core   +1 more source

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

open access: yesAdvanced 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   +3 more
wiley   +1 more source

Designing Polymer Nanocomposites for X‐Ray Shielding: Mechanisms, Architectures, and Scalable Processing

open access: yesAdvanced Engineering Materials, EarlyView.
This review highlights advances in lightweight, lead‐free polymer nanocomposites for diagnostic X‐ray shielding. By linking filler chemistry, dispersion, architecture, and photon interaction mechanisms, it establishes structure–performance relationships guiding material design.
Aklilu G. Messele   +2 more
wiley   +1 more source

Self similar stochastic processes [PDF]

open access: yes, 1982
The thesis is concerned with the development of a representation and the behaviour of self similar stochastic processes, and for the testing of a Gaussian sequence with finite variance for self similarity.
Harte, David
core  

Efficient simulation of stochastic chemical kinetics with the Stochastic Bulirsch-Stoer extrapolation method [PDF]

open access: yes, 2014
BackgroundBiochemical systems with relatively low numbers of components must be simulated stochastically in order to capture their inherent noise. Although there has recently been considerable work on discrete stochastic solvers, there is still a need ...
Barrio Solórzano, Manuel   +11 more
core   +1 more source

Comparison of Triply Periodic Minimal Surface Energy Absorbers Under Uniaxial Compressive Loading

open access: yesAdvanced Engineering Materials, EarlyView.
This study investigates LCD 3D printed Triply Periodic Minimal Surface (TPMS) structures as mechanical energy absorbers. By comparing various base designs and layered combinations under uniaxial compression, it identifies that a Diamond‐Gyroid sandwich structure offers superior performance.
Sergej Grednev   +2 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

open access: yesAdvanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle   +5 more
wiley   +1 more source

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