Results 131 to 140 of about 351,687 (262)

Representation learning approach for understanding structured documents. [PDF]

Sci Rep
Trivedi A, Khanna S, Chaudhury S, Harit G.   +3 more
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Utility-Leakage Trade-Off for Federated Representation Learning. [PDF]

Entropy (Basel)
Liu Y, Günlü O, Shi Y, Wu Y.
europepmc   +1 more source

A Concept of a Digital and Traceable Manufacturing Documentation Based on Formalized Process Description Applied on Composite Aircraft Moveable

Advanced Engineering Materials, EarlyView.
The documentation of component manufacture has become an essential part of today's production processes, especially for the analysis and optimization of production or component design with regard to structural performance, economic efficiency, and sustainability.
Björn Denker, Dominik Delisle, Christian Krombholz, Martin Rädel, Frank Dressel   +4 more
wiley   +1 more source

Learning Disentangled Representations

, 2022
Artificial intelligence systems are seeking to learn better representations. One of the most desirable properties in these representations is disentanglement. Disentangled representations show merits of interpretability and generalizability. Through these representations, the world around us can be decomposed into explanatory factors of variation, and ...
openaire   +1 more source

A systematic review of molecular representation learning foundation models. [PDF]

Brief Bioinform
Song B, Zhang J, Liu Y, Liu Y, Jiang J, Yuan S, Zhen X, Liu Y.   +7 more
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

An equivariant pretrained transformer for unified 3D molecular representation learning. [PDF]

Nat Commun
Jiao R, Kong X, Zhang L, Yu Z, Ren F, Tan W, Huang W, Liu Y.   +7 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

Fair CCA for Fair Representation Learning: An ADNI Study. [PDF]

ACM BCB
Hou B, Wang Z, Zhou Z, Tong B, Wang Z, Bao J, Duong-Tran D, Long Q, Shen L.   +8 more
europepmc   +1 more source