Results 181 to 190 of about 110,431 (303)

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

UbiQTree: Uncertainty quantification in XAI with tree ensembles. [PDF]

Patterns (N Y)
Dubey A, Anžel A, İlgen B, Hattab G.
europepmc   +1 more source

Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure

Advanced Engineering Materials, EarlyView.
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari, Supharoek Sornsomrit, Muhammad Ayaz Akbar, Stephan Rudykh   +3 more
wiley   +1 more source

Hybridndiff-UQ: Uncertainty quantification for hybrid neural differentiable modeling. [PDF]

Theor Appl Mech Lett
Akhare D, Luo T, Wang JX.
europepmc   +1 more source

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

Advanced 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, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

Multivariate and Online Transfer Learning With Uncertainty Quantification. [PDF]

Stat Med
Hickey J, Williams JP, Reich BJ, Hector EC.   +3 more
europepmc   +1 more source

Microstructure, Thermal Transport, and Dry‐Sliding Tribology of Powder‐Metallurgy Al7075 Composites Reinforced With Sol–Gel‐Derived ZnO–rGO Hybrid Nanoparticles

Advanced Engineering Materials, EarlyView.
Sol–gel‐derived ZnO–rGO hybrid nanoparticles enable Al7075 powder‐metallurgy composites to achieve concurrent gains in hardness and thermal conductivity while markedly lowering friction and wear. The hybrid architecture couples ZnO‐based load support with rGO‐assisted lamellar sliding and heat spreading, revealing a promising route toward lightweight ...
Bunyamin Aksakal, Cevher Kursat Macit, Merve Ayık, Hakan Tutumlu   +3 more
wiley   +1 more source

Uncertainty Quantification of Central Canal Stenosis Deep Learning Classifier From Lumbar Sagittal T2-Weighted MRI. [PDF]

JOR Spine
Brenzikofer A, Monzon M, Galbusera F, Manjaly ZM, Cina A, Jutzeler CR.   +5 more
europepmc   +1 more source

Counterion Dependent Side‐Chain Relaxation Stiffens a Chemically Doped Thienothiophene Copolymer

Advanced Functional Materials, EarlyView.
Oxidation of a thienothiophene copolymer, p(g3TT‐T2), via different doping strategies and dopant molecules resulted in materials with similar oxidation levels and a high electrical conductivity of ≈100 S cm−1. However, mechanical properties varied significantly, with sub‐glass transition temperatures and elastic moduli spanning from –44°C to –3°C and ...
Mariavittoria Craighero, Meghna Jha, Elsa Veronica Flores‐Vela, Joost Kimpel, Andreas Schaefer, Jiali Guo, Jesika Asatryan, Alberto Peinador Veiga, Shuichi Haraguchi, Per‐Anders Carlsson, Jaime Martín, Mariano Campoy‐Quiles, Christian Müller   +12 more
wiley   +1 more source

Benchmarking the performance of uncertainty quantification methods for neural network-based interatomic potentials. [PDF]

J Cheminform
Wimer NT, Mueller J, Hamel S, Lordi V.
europepmc   +1 more source