Results 211 to 220 of about 336,714 (315)

Amplitude Versus Angle (AVA) feature restoration in prestack gathers via dictionary learning. [PDF]

PLoS One
Gao Y, Shi X, Zhang D, Wang C, Zhang R, Feng Y.   +5 more
europepmc   +1 more source

RMSE, MAE, , and MAPE of combination models for wheat price forecasting.

Kaixuan Sun (4739976), Qi Yao (555617), Yanhui Li (431807)   +2 more
openalex   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Integrating biological and machine learning models for rainbow trout growth: Balancing accuracy and interpretability. [PDF]

PLoS One
Fulton L, Lyu P.
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

Short-Term Forecast of Tropospheric Zenith Wet Delay Based on TimesNet. [PDF]

Sensors (Basel)
Zhao X, Gu S, Mi J, Dong J, Xiao L, Chu B.   +5 more
europepmc   +1 more source

Comparison of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) for different algorithms.

Jun Zhou (6477), Nuo Xu (130308), Xuexuan Wu (14294080)   +2 more
openalex   +1 more source

Multi‐Property Machine Learning Models to Accelerate the Transition Toward Bio‐Based Emulsion Polymers

Advanced Intelligent Discovery, EarlyView.
A machine learning framework simultaneously predicts four critical properties of monomers for emulsion polymerization: propagation rate constant, reactivity ratios, glass transition temperature, and water solubility. These tools can be used to systematically identify viable bio‐based monomer pairs as replacements for conventional formulations, with ...
Kiarash Farajzadehahary, Nicholas Ballard   +1 more
wiley   +1 more source

Out-of-distribution evaluation of active learning pipelines for molecular property prediction. [PDF]

RSC Adv
Yin T, Gao P, Panapitiya G, Saldanha EG.
europepmc   +1 more source

MAEs and RMSEs for the bridge model and our proposed regression models.

Zhiqiang Lan (10306306), Zidi Liu (6862967), Guoyao Wu (147327)   +2 more
openalex   +1 more source