Results 161 to 170 of about 1,302,044 (285)

Benchmarking Radiation Transport Monte Carlo Simulations with MCNP and Geant4 Using High Performance Computing. [PDF]

Health Phys
Moats K, Desrosiers M, Linton G, Wood R.
europepmc   +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

Accelerating mesh-based Monte Carlo simulations using contemporary graphics ray-tracing hardware. [PDF]

J Biomed Opt
Yan S, Dwyer D, Kaeli DR, Fang Q.
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

Operating characteristics of agreement metrics in AI-based scoring: a Monte Carlo simulation. [PDF]

Front Psychol
Yandi A.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Computational nanobody design using graph neural networks and Metropolis Monte Carlo sampling. [PDF]

Brief Bioinform
Wang L, He X, Qian X, Guo G, Huang Q.
europepmc   +1 more source

Monte Carlo and Quasi-Monte Carlo Sampling [PDF]

, 2009
openaire   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Commissioning and verification of a 3D Monte Carlo independent calculation software for O-ring linac systems. [PDF]

J Appl Clin Med Phys
Meng X, Dong T, Liang Y, Zhou Z, Wang Z, Qiu J, Zhao J, Zhang Z, Li W, Yang B.   +9 more
europepmc   +1 more source