Results 211 to 220 of about 1,407,468 (324)

Comparison of sampling plans by variables using the bootstrap and Monte Carlo simulations

, 2014
Fernanda Figueiredo, Adelaide Figueiredo, M. Ivette Gomes   +2 more
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

From Bray-Curtis ordination to Markov Chain Monte Carlo simulation: assessing anthropogenically-induced and/or climatically-induced changes in arboreal ecosystems

, 2013
Buddhika Madurapperuma
openalex   +1 more source

Modeling the shape and stability of supported Co nanoparticles under Fischer–Tropsch conditions via DFT calculations and Monte Carlo simulations: insights into CO-driven surface reconstruction

Sireci, Enrico, Grüger, Tilman D., Plessow, Philipp N., Sharapa, Dmitry I., Studt, Felix   +4 more
openalex   +1 more source

Kinetic energy classification and smoothing for compact B-spline basis sets in quantum Monte Carlo

, 2018
Jaron T. Krogel, Fernando A. Reboredo
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

Uncertainty-aware genomic classification of Alzheimer's disease: a transformer-based ensemble approach with Monte Carlo dropout. [PDF]

Brief Bioinform
Jo T, Lee EH, Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Alzheimer’s Disease Sequencing Project (ADSP).   +2 more
europepmc   +1 more source

Alternative meta-analysis estimators compared in Monte Carlo simulation experiments.

Stephen C. Newbold (566260), Chris Dockins (21540129), Nathalie Simon (6189494), Kelly Maguire (12088698), Abdullah Muhammad Sakib (21540132)   +4 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

Comparative reliability assessment of PET and UTCI thermal comfort indices using Monte Carlo simulation in urban microclimates. [PDF]

Sci Rep
Sargazi MA, Heidari A, Davtalab J, Piri J.   +3 more
europepmc   +1 more source