Results 151 to 160 of about 3,156,569 (293)

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

Validation of a computational model of bone conduction sound reception in mysticetes. [PDF]

PLoS One
Krysl P, Morris MA, Hildebrand JA, Cranford TW.   +3 more
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

A Vector-Based Computational Model of Multimodal Insect Learning Walks. [PDF]

Biomimetics (Basel)
Xiang Z, Sun X, Peng J.
europepmc   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Development of an Explainable Machine Learning Computational Model for the Prediction of Severe Complications After Orchiectomy in Stallions. [PDF]

Animals (Basel)
Tyrnenopoulou P, Kalatzis D, Kiouvrekis Y, Flouraki E, Folias L, Loukopoulos E, Starras A, Chalvatzis P, Tsioli V, Mavrogianni VS, Fthenakis GC.   +10 more
europepmc   +1 more source

A Thermodynamic 3D Model for the Simulation of Diffusion‐Controlled Alloying Processes in Heterogeneous Material Structures

Advanced Engineering Materials, EarlyView.
A numerical model resulting from irreversible thermodynamics for describing transport processes is introduced, focusing on thermodynamic activity gradients as the actual driving force for diffusion. Implemented in CUDA C++ and using CalPhaD methods for determining the necessary activity data, the model accurately simulates interdiffusion in aluminum ...
Ulrich Holländer, Leonhard Holländer, André Langohr, Hans Jürgen Maier   +3 more
wiley   +1 more source

Average Responses of Brain Displacement Under Rotational Loading for Computational Model Validation. [PDF]

IEEE Trans Biomed Eng
Alshareef AA, Giudice JS, Wu T, Panzer MB.   +3 more
europepmc   +1 more source

EBSD Study of Creep‐Induced Lattice Misorientation in MgO‐Particle‐Reinforced Austenitic Steel Composites

Advanced Engineering Materials, EarlyView.
Creep experiments at 900°C on coarse‐grained steel‐ceramic composites containing recycled magnesia reveal that higher ceramic volume fractions significantly enhance the creep resistance. Detailed EBSD investigations identify subgrain formation in the steel matrix as the dominant deformation mechanism.
Moritz Müller, Amaya Compart, Mahnaz Mehdizadehlima, Serhii Yaroshevskyi, Christos G. Aneziris, Horst Biermann, Anja Weidner   +6 more
wiley   +1 more source

A computational model of radial scaffolds in the human fetal brain based on MRI. [PDF]

Cereb Cortex
Homae F, Schwartz E, Tsuzuki D, Gima H, Watanabe H, Prayer D, Kasprian G, Langs G, Taga G.   +8 more
europepmc   +1 more source