Results 171 to 180 of about 108,134 (338)

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

sandwich: Robust Covariance Matrix Estimators [PDF]

, 2004
Achim Zeileis, Thomas Lumley
openalex   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Multivariate Regression With Dependence Structures: Evaluating Associations Between Plasma Metabolomics and Alcohol Intake in Older Adults. [PDF]

Stat Med
Yang Y, Chen C, Lee H, Wang M, Zhang Y, Chen S.   +5 more
europepmc   +1 more source

Fast calculations of Jackknife covariance matrix estimator

, 2021
Vitaliy Miroshnychenko
openalex   +2 more sources

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source

Bayesian perspective for orientation determination in cryo-EM with application to structural heterogeneity analysis. [PDF]

Acta Crystallogr D Struct Biol
Xu S, Balanov A, Singer A, Bendory T.
europepmc   +1 more source

A Closed-Form Asymptotic Variance-Covariance Matrix for the Quasi-Maximum Likelihood Estimator of the Garch(1,1) Model

, 2008
Jun Ma
openalex   +1 more source

Accelerating Surface Composition Characterization of Thin‐Film Materials Libraries Using Multi‐Output Gaussian Process Regression

Advanced Intelligent Discovery, EarlyView.
To integrate surface analysis into materials discovery workflows, Gaussian process regression is used to accurately predict surface compositions from rapidly acquired volume composition data (obtained by energy‐dispersive X‐ray spectroscopy), drastically reducing the number of required surface measurements on thin‐film materials libraries.
Felix Thelen, Florian Lourens, Alfred Ludwig   +2 more
wiley   +1 more source

Mediation analysis with graph mediator. [PDF]

Biostatistics
Xu Y, Zhao Y.
europepmc   +1 more source