Results 71 to 80 of about 1,338,246 (278)

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

open access: yesAdvanced 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   +8 more
wiley   +1 more source

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

open access: yesAdvanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari   +5 more
wiley   +1 more source

Graph Drawing by Stochastic Gradient Descent [PDF]

open access: yesIEEE Transactions on Visualization and Computer Graphics, 2019
Submitted to IEEE Transactions on Visualization and Computer Graphics on 26/06 ...
Jonathan X. Zheng   +2 more
openaire   +5 more sources

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

open access: yesAdvanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi   +3 more
wiley   +1 more source

A hardware acceleration technique for gradient descent and conjugate gradient [PDF]

open access: yes, 2011
Gradient descent, conjugate gradient, and other iterative algorithms are a powerful class of algorithms; however, they can take a long time for conver- gence.
Kesler, David R.
core  

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

open access: yesAdvanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare   +5 more
wiley   +1 more source

Attentional-Biased Stochastic Gradient Descent

open access: yesTrans. Mach. Learn. Res., 2020
In this paper, we present a simple yet effective provable method (named ABSGD) for addressing the data imbalance or label noise problem in deep learning. Our method is a simple modification to momentum SGD where we assign an individual importance weight to each sample in the mini-batch.
Qi Qi 0006   +4 more
openaire   +3 more sources

Multilayer Self‐Limiting Electrospray Deposition via Stepped Voltage Bias

open access: yesAdvanced Engineering Materials, EarlyView.
Self‐limiting electrospray deposition (SLED) uses a high voltage to generate and deposit a charged payload on a target surface. The coating retains its charge, repelling newly arriving material. SLED thickness can be decreased by applying a secondary bias to the target.
Madhuri Deb   +3 more
wiley   +1 more source

A new approach to training neural networks using natural gradient descent with momentum based on Dirichlet distributions

open access: yesКомпьютерная оптика, 2023
In this paper, we propose a natural gradient descent algorithm with momentum based on Dirichlet distributions to speed up the training of neural networks. This approach takes into account not only the direction of the gradients, but also the convexity of
R.I. Abdulkadirov, P.A. Lyakhov
doaj   +1 more source

Simulation‐Based Analysis of Insert Pull‐Out in Nickel‐Polyurethane Hybrid Foams Using CT‐Derived Geometries

open access: yesAdvanced Engineering Materials, EarlyView.
CT‐based finite element simulations combined with in situ X‐ray computed tomography are used to analyze insert pull‐out in nickel‐coated polymer foams. Despite variations in material parameters, deformation consistently concentrates within a narrow annular region around the insert.
Yannik Bautz   +4 more
wiley   +1 more source

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