Results 71 to 80 of about 110,828 (307)
Near‐Field Electrospinning Micro‐Printhead Achieves Precise Control of Nanofiber Deposition
Advanced Engineering Materials, EarlyView.A micro‐printhead for near‐field electrospinning enables reproducible deposition of polymer nanofibers with diameters below 50 nm. Systematic parameter studies uncover the mechanisms linking operating conditions to fiber morphology, paving the way for precise and low‐cost nanoscale 3D manufacturing.As a high‐resolution, cost‐effective, and rapid ...
Han Xu, Dario Mager, Jan G. Korvink
wiley +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
Non-stationary log-periodogram regression [PDF]
, 1998 We study asymptotic properties of the log-periodogram semiparametric estimate of the memory parameter d for non-stationary (d>=1/2) time series with Gaussian increments, extending the results of Robinson (1995) for stationary and invertible Gaussian ...
Velasco Gómez, Carlos +2 more
core +1 more source
Improving soil moisture prediction using Gaussian process regression
Smart Agricultural TechnologySoil moisture plays a vital role in agriculture and hydrology, influencing key processes like plant growth and evaporation. Recent advancements in soil moisture monitoring have improved our ability to measure it at different scales, but challenges ...
Xiaomo Zhang, Xin Sun, Zhulu Lin
doaj +1 more source
Gaussian Process Regression with Local Explanation
CoRR, 2020 Gaussian process regression (GPR) is a fundamental model used in machine learning. Owing to its accurate prediction with uncertainty and versatility in handling various data structures via kernels, GPR has been successfully used in various applications.
Yuya Yoshikawa, Tomoharu Iwata
openaire +2 more sources
Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source
An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials
Advanced Engineering Materials, EarlyView.An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut +16 more
wiley +1 more source
Gaussian Process Regression´s Hyperparameters Optimization to Predict Financial Distress
Retos, 2023 Predicting financial distress has become one of the most important topics of the hour that has swept the accounting and financial field due to its significant correlation with the development of science and technology.
Jakub Horak, Amine Sabek
doaj +1 more source
Advanced Engineering Materials, EarlyView.Coarse‐grained (left) and atomistic (right) models of the shape memory polymer ESTANE ETE 75DT3 are shown schematically. The two representations bridge molecular detail and mesoscopic description. Both models capture shape memory behavior, linking segmental mobility and conformational relaxation of anisotropic chains to macroscopic recovery, and ...
Fathollah Varnik
wiley +1 more source
Gaussian Processes and Fast Matrix-Vector Multiplies [PDF]
, 2009 Gaussian processes (GPs) provide a flexible framework for probabilistic regression. The necessary computations involve standard matrix operations.
Murray, Iain, Murray, Iain; id_orcid
core