Results 221 to 230 of about 173,069 (310)

Monte Carlo method using AB initio total energy calculation

, 2006
Luana S. Pedroza
openalex   +1 more source

Using Monte Carlo Method to Study Magnetic Properties of Frozen Ferrofluid

, 2011
Tran Hung Nguyen, Hoang Vinh Tran
openalex   +2 more sources

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Application of the adaptive Monte Carlo method for uncertainty evaluation in the determination of total testosterone in human serum by triple isotope dilution mass spectrometry. [PDF]

Anal Bioanal Chem
Liu G, Wang H, Han Y, Liu C, Liang M.
europepmc   +1 more source

The Effect of a Number of Selective Points in Modelling of Polymerization Reaction by Monte Carlo Method: Studying the Initiation Reaction

, 2002
Maryam Sadi, Abbas Shahrabadi, Bahram Dabir   +2 more
openalex   +2 more sources

Sampling Strategy: An Overlooked Factor Affecting Artificial Intelligence Prediction Accuracy of Peptides’ Physicochemical Properties

Advanced Intelligent Discovery, EarlyView.
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan, Ankeer Abuduhebaier, Haojin Zhou, Jiaqi Wang   +3 more
wiley   +1 more source

Advances in Monte Carlo Method for Simulating the Electrical Percolation Behavior of Conductive Polymer Composites with a Carbon-Based Filling. [PDF]

Polymers (Basel)
Zhang Z, Hu L, Wang R, Zhang S, Fu L, Li M, Xiao Q.   +6 more
europepmc   +1 more source

Novel Approach to Uncertainty Study of Microseismic Hypocentres Using Bootstrap and Monte Carlo Methods in the Patuha Geothermal Field

Djoko A. Wibowo, Rendy Delliansyah, Chevy Iskandar, Istiawan Nurpratama   +3 more
openalex   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source