Results 221 to 230 of about 708,503 (346)

Quasi-Monte Carlo Methods Applied to Tau-Leaping in Stochastic Biological Systems. [PDF]

Bull Math Biol, 2019
Beentjes CHL, Baker RE.
europepmc   +1 more source

A General Monte Carlo Method for Mapping Multiple Quantitative Trait Loci

, 1996
Ritsert C. Jansen
openalex  

Simulation and the Monte Carlo method

Wiley series in probability and mathematical statistics, 1981
R. Rubinstein
semanticscholar   +1 more source

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

Diffusion Equation-Assisted Markov Chain Monte Carlo Methods for the Inverse Radiative Transfer Equation. [PDF]

Entropy (Basel), 2019
Li Q, Newton K.
europepmc   +1 more source

Fermion-dimer scattering using an impurity lattice Monte Carlo approach and the adiabatic projection method [PDF]

, 2014
Serdar Elhatisari, Dean Lee
openalex   +1 more source

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

Segmenting Proteins into Tripeptides to Enhance Conformational Sampling with Monte Carlo Methods. [PDF]

Molecules, 2018
Denarie L, Al-Bluwi I, Vaisset M, Siméon T, Cortés J.   +4 more
europepmc   +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

Comprehensive benchmarking of Markov chain Monte Carlo methods for dynamical systems. [PDF]

BMC Syst Biol, 2017
Ballnus B, Hug S, Hatz K, Görlitz L, Hasenauer J, Theis FJ.   +5 more
europepmc   +1 more source