Sentence examples for explicit predictor from inspiring English sources

This paper focuses on the formulation and implementation of explicit predictor– multicorrector Time Discontinuous Galerkin methods for linear structural dynamics.

The PCICE-FEM scheme is developed as a predictor corrector scheme by performing a fractional-step splitting of the semi-implicit temporal discretization of the governing equations into an explicit predictor phase and a semi-implicit pressure-correction phase coupled by a pressure Poisson solution.

The PCICE-FEM scheme combines a modified form of the two-step Taylor Galerkin FEM scheme as an explicit predictor for the fractional momentum equations and a time-weighted FEM method for the semi-implicit form of the mass conservation and the total energy equations.

We use an information theoretic approach to model spatial variation in these costs using a range of plausible, spatially explicit predictor variables, including a novel measure of anthropogenic pressure that measures the human pressure that accrues to any point in the landscape by taking into account all people in the landscape, inversely weighted by their distance to that point.

These LiDAR TCH data from Hawaii Island were used to calibrate a Random Forest Machine Learning (RFML) model, which was subsequently used to predict TCH at 30 m resolution on all islands from a portfolio of spatially explicit predictor maps (Additional file 1: Figures S2-S4).

Disease risk maps are traditionally developed as an extension of a statistical modelling process in which geographically explicit predictor variables are used to estimate the probability of disease occurrence [ 11- 13].

Unlike the KF, the MHE formulation does not use an explicit predictor-corrector approach.

Under an additional assumption, explicit nested predictor feedback controllers involving only 1-fold integrals are established.

The results, obtained using these low order formulas, are compared with the results obtained using the explicit Adams Bashforth predictor-corrector method.

In this study, we used publicly available GIS layers and statistical tree-based modeling (CART and Random Forest) to predict pathogen indicator counts at a regional scale using 88 spatially explicit landscape predictors and 6657 samples from non-estuarine streams in the Oregon Coast Range.

Although the examples presented in Ovaskainen et al. (2010) and Sebastian-Gonzalez et al. (2010) account for spatially explicit environmental predictors and species' occurrences, they do not use information about the neighbourhood surrounding observation points, which may contain important ecological information shaped by relationships between interacting species.