Results 101 to 110 of about 56,902 (196)
A Guide to Bayesian Optimization in Bioprocess Engineering
Biotechnology and Bioengineering, Volume 123, Issue 4, Page 805-830, April 2026.ABSTRACT Bayesian optimization has become widely popular across various experimental sciences due to its favorable attributes: it can handle noisy data, perform well with relatively small data sets, and provide adaptive suggestions for sequential experimentation.
Maximilian Siska +5 more
wiley +1 more source
A Penalized Orthogonal Kriging Method for Selecting a Global Trend
AxiomsA kriging regression model is a popular and effective type of surrogate model in computer experiments. A significant challenge arises when the mean function of the model includes polynomial terms with unknown coefficients, leading to identifiability ...
Xituo Zhang +3 more
doaj +1 more source
Combining the Finite Element Analysis and Kriging Model for Study on Laser Surface Hardening Parameters of Pitch Bearing Raceway. [PDF]
Materials (Basel), 2022 Zhang H, Zhu M, Ji S, Dou Y.
europepmc +1 more source
The Canadian Journal of Chemical Engineering, Volume 104, Issue 4, Page 1769-1795, April 2026.Research frontiers in using biochar for heavy metal remediation. Abstract Heavy metal contamination of water has long been a serious environmental issue. Biochar and biochar‐based composites are emerging as effective and sustainable solutions for heavy metal removal due to their strong adsorption abilities and environmentally friendly nature.
Soumik Chakma +4 more
wiley +1 more source
Reliability-based design optimization using kriging surrogates and subset simulation
, 2011 The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate.
Bourinet, J. -M. +2 more
core +3 more sources
Ecology and Evolution, Volume 16, Issue 4, April 2026.We evaluated how landscape features, including fire, influence genetic connectivity for three small mammal species in northern Australian savannas. Using resistance surface modelling across two islands with contrasting disturbance regimes, we found that fire, rainfall, and topography affected gene flow in species‐ and scale‐specific ways.
Alexander R. Carey +7 more
wiley +1 more source
Comparison of Gaussian process modeling software [PDF]
, 2016 Gaussian process fitting, or kriging, is often used to create a model from a set of data. Many available software packages do this, but we show that very different results can be obtained from different packages even when using the same data and model ...
Ankenman, Bruce E. +2 more
core +2 more sources
DCENT‐I: A Globally Infilled Extension of the Dynamically Consistent ENsemble of Temperature Dataset
Geoscience Data Journal, Volume 13, Issue 2, April 2026.DCENT‐I infills data gaps in DCENT, producing spatially coherent temperature fields (top) and a slightly higher GMST warming estimate (bottom). Top: December 1877 temperature anomalies (°C; 1961–1990 December baseline) from DCENT (left) and DCENT‐I (right). Bottom: GMST before (DCENT, blue) and after (DCENT‐I, red) infilling.
Duo Chan +8 more
wiley +1 more source
, 2017 Optimal design under uncertainty has gained much attention in the past ten years due to the ever increasing need for manufacturers to build robust systems at the lowest cost.
Bourinet, J. -M. +2 more
core
GloMarGridding: A Python Toolkit for Flexible Spatial Interpolation in Climate Applications
Geoscience 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