Results 111 to 120 of about 20,996 (261)

On a novel approach for optimizing composite materials panel using surrogate models [PDF]

open access: yes, 2011
This paper describes an optimization procedure to design thermoplastic composite panels under axial compressive load conditions. Minimum weight is the goal. The panel design is subject to buckling constraints.
Akcay-Perdahcioglu, D.   +3 more
core   +1 more source

GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications

open access: yesGeoscience Data Journal, Volume 13, Issue 2, April 2026.
Global surface climate datasets contain structural uncertainty that is difficult to attribute to individual processing steps. We present GloMarGridding, a Python package that isolates the spatial interpolation component using Gaussian Process Regression (or kriging) to generate spatially complete fields and uncertainty estimates. The techniques used in
Richard C. Cornes   +6 more
wiley   +1 more source

A Hydrogeophysical Survey Utilising the sTEM and sTEMprofiler Systems: Transient Electromagnetic Data and Resistivity Models

open access: yesGeoscience Data Journal, Volume 13, Issue 2, April 2026.
We present a transient electromagnetic dataset from northwestern Denmark acquired with the novel sTEMprofiler system, mapping buried Quaternary valleys. The open dataset, including raw data and resistivity models, enables benchmarking of inversion methods and integration into geological and hydrological studies. ABSTRACT Transient electromagnetic (TEM)
Line M. Madsen   +3 more
wiley   +1 more source

Estimasi Kandungan Hasil Tambang Menggunakan Ordinary Indicator Kriging [PDF]

open access: yes, 2013
Kriging is a geostatistical analysis of the data used to estimate the value that represents a no sample point based sample point in the surrounding by considering the spatial correlation in the data.
Awali, A. A. (Aldila)   +2 more
core  

Imputing Missing Long‐Term Spatiotemporal Multivariate Atmospheric Data With CNN‐Transformer Machine Learning

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Continuous physical domains are important for scientific investigations of dynamical processes in the atmosphere. However, missing data—arising from operational constraints and adverse environmental conditions—pose significant challenges to accurate analysis and modeling.
Jiahui Hu, Wenjun Dong, Alan Z. Liu
wiley   +1 more source

Geostatistical interpretation of paleoceanographic data over large ocean basins - Reality and fiction [PDF]

open access: yes, 1998
A promising approach to reconstruct oceanographic scenarios of past time slices is to drive numerical ocean circulation models with sea surface temperatures, salinities, and ice distributions derived from sediment core data.
Hervada-Sala, C.   +3 more
core  

Efficient Kilometer‐Scale Precipitation Downscaling With Conditional Wavelet Diffusion

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 2, April 2026.
Abstract Precipitation products such as Integrated Multi‐satellitE Retrievals have coarse resolution (∼10 ${\sim} 10$ km), which limits their application in hydrological modeling and extreme weather analysis. We propose the Wavelet Diffusion Model (WDM), a fast generative framework for high‐quality precipitation downscaling trained on multi‐radar multi‐
Chugang Yi   +4 more
wiley   +1 more source

Variogram investigation of covariance shape within longitudinal data with possible use of a krigeage technique as an interpolation tool: Sheep growth data as an example [PDF]

open access: yes, 2014
peer-reviewedMost quantitative traits considered in livestock evolve over time and several continuous functions have been proposed to model this change.
Chalh, A., El Gazzah, M.
core  

Coarse‐to‐Fine Spatial Modeling: A Scalable, Machine‐Learning‐Compatible Framework

open access: yesGeographical Analysis, Volume 58, Issue 2, April 2026.
ABSTRACT This study proposes coarse‐to‐fine spatial modeling (CFSM) as a scalable and machine learning‐compatible alternative to conventional spatial process models. Unlike conventional covariance‐based spatial models, CFSM represents spatial processes using a multiscale ensemble of local models.
Daisuke Murakami   +5 more
wiley   +1 more source

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