Results 141 to 150 of about 23,025 (301)

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
wiley   +1 more source

Multi-Level Hybrid Monte Carlo / Deterministic Methods for Particle Transport Problems [PDF]

Vincent N. Novellino, Dmitriy Y. Anistratov   +1 more
openalex   +1 more source

Longitudinal development of extensive air showers: Hybrid code SENECA and full Monte Carlo [PDF]

, 2005
Jeferson A. Ortiz, Gustavo Medina‐Tanco, V. de Souza   +2 more
openalex   +1 more source

Constrained hybrid Monte Carlo algorithms for gauge–Higgs models [PDF]

, 2020
Michael Günther, Roman Höllwieser, Francesco Knechtli   +2 more
openalex   +1 more source

A hybrid Monte Carlo study of bond-stretching electron-phonon interactions and charge order in BaBiO$_3$ [PDF]

, 2022
Benjamin Cohen-Stead, Kipton Barros, Richard T. Scalettar, Steven Johnston   +3 more
openalex   +1 more source

A review on rockburst prediction and prevention to shape an ontology‐based framework for better decision‐making for underground excavations

Deep Underground Science and Engineering, EarlyView.
A scientometric analysis of 2449 journal articles and a comprehensive review of 336 papers were conducted, discussing and identifying challenges and research gaps in rockburst prediction and prevention and proposing an ontology‐based framework for better decision‐making in underground excavations. Abstract With underground engineering projects becoming
Hongchuan Yan, Haijiang Li, Hongqiang Xie, Mingli Xiao, Jianliang Pei, Huaizhong Liu, Jiangda He   +6 more
wiley   +1 more source

Enhanced Numerical Solutions for Fractional PDEs Using Monte Carlo PINNs Coupled with Cuckoo Search Optimization

Fractal and Fractional
In this study, we introduce an innovative approach for addressing fractional partial differential equations (fPDEs) by combining Monte Carlo-based physics-informed neural networks (PINNs) with the cuckoo search (CS) optimization algorithm, termed PINN-CS.
Tauqeer Ahmad, Muhammad Sulaiman, David Bassir, Fahad Sameer Alshammari, Ghaylen Laouini   +4 more
doaj   +1 more source

Lattice benchmarking of deterministic, Monte Carlo and hybrid Monte Carlo reactor physics codes for the soluble-boron-free SMR cores

, 2019
Syed Bahauddin Alam, Dinesh Kumar, Bader Almutairi, Tuhfatur Ridwan, Cameron S. Goodwin, Geoffrey T. Parks   +5 more
openalex   +2 more sources

Slip versus dilation tendency parameter space as a tool for prospecting structurally controlled geothermal systems in greenfield: insights from the greater Ruhr region

Deep Underground Science and Engineering, EarlyView.
This study highlights that with the static and time‐dependent evolution of slip versus dilation tendency parameter space, structurally controlled deep geothermal systems can be selected within areas of higher permeability and lower seismic hazard. Abstract Faults play a vital role in the Earth's hydraulic system by facilitating fluid flow when dilating
Michal Kruszewski, Alessandro Verdecchia, Oliver Heidbach, Rebecca M. Harrington, David Healy   +4 more
wiley   +1 more source

Precision planter monitoring system based on mobile communication network

IET Networks, EarlyView., 2022
Abstract Sowing is an important link in agricultural production and the basis for ensuring high yields and bumper harvests. Agriculture requires precision plows with good performance and stable work. However, the seeding process is in a completely closed state, and the operator relies mainly on experience to judge the operating state and performance of
Bing Li, Jiyun Li
wiley   +1 more source