Results 91 to 100 of about 1,981,405 (253)

Parameter tuning for the NFFT based fast Ewald summation

Frontiers in Physics, 2016
The computation of the Coulomb potentials and forces in charged particle systems under 3d-periodic boundary conditionsis possible in an efficient way by utilizing the Ewald summation formulas and applying the fast Fourier transform (FFT).
Franziska Nestler
doaj   +1 more source

Tuning Parameter Tolerance Design: Foundations, Methods, and Measures [PDF]

, 2000
In this paper, a novel technique is presented to solve tolerance design problems. To achieve the desired performance tolerance, the technique uses a subtle, but significant, change in the design: the addition of a tuning parameter in place of an increase
McAdams, Daniel A., Wood, Kristin L.
core   +1 more source

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

Methods of frequency tuning vibration based micro-generator

, 2009
A vibration based micro-generator is an energy harvesting device that couples a certain transduction mechanism to the ambient vibration and converts mechanical energy to electrical energy.
Zhu, Dibin
core   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

IMPLEMENTATION OF PROPHET IN AMERICAN ELECTRICITY FORECASTING WITH AND WITHOUT PARAMETER TUNING

Media Statistika
Prophet is one of the machine learning approximation methods that accommodate trends, seasonality, and holiday impacts in time series data. Generally, the performance of machine learning models can be improved by implementing hyperparameter tuning.
Winita Sulandari, Yudho Yudhanto, Riskhia Hapsari, Monica Dini Wijayanti, Hilman Ferdinandus Pardede   +4 more
doaj   +1 more source

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

Advanced 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, Noah‐Lean Roschlaub, Anne Jung   +2 more
wiley   +1 more source

Revisiting Differentially Private Hyper-parameter Tuning [PDF]

We study the application of differential privacy in hyper-parameter tuning, a crucial process in machine learning involving selecting the best hyper-parameter from several candidates.
Xiang, Zihang, Wang, Chenglong, Wang, Di, Wang, Tianhao   +3 more
core   +1 more source

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

Advanced 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, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source