Sentence examples for methods we explore from inspiring English sources
In addition to investigating more exhaustive local search methods we explore the use of techniques derived from research on discretizing continuous state spaces.
Using the above methods, we explore the model space and compare outputs to decide on the optimal model, given the data.
For the sake of designing symplectic csRKN methods, we explore the sufficient conditions for symplecticity, and we show a simple way to derive symplectic RKN-type integrators by using Legendre polynomial expansion.
We show how to derive asymptotic-preserving (AP) schemes of arbitrary order, and in particular by using the Shu Osher representation of Runge Kutta methods we explore the monotonicity properties of such schemes, like strong stability preserving (SSP) and positivity preserving.
Focusing on the performance of time-dependent density functional theory but using also the ab initio CIS(D), CC2 and CASPT2 methods, we explore how various methodological considerations influence computed spectra and find, somewhat surprisingly, that density functionals lacking exact exchange reproduce the experimental measurements with smaller errors than functionals that include exact exchange.
Hence, to more rigorously assess the relative performance of different prediction methods, we explore the use of similarity-reduced datasets.
In order to obtain more realistic estimates of performance of MHC-II binding peptide prediction methods, we explored several methods for constructing similarity-reduced MHC-II datasets.
However, all summary methods we explored were impacted by gene tree estimation error.
Mapping is a combination of modeling and feature selection, and the methods we explored in this work address the mapping problem.
Considering the complexities in applying either of these two methods, we explored the feasibility of reducing image stretching by simply controlling the drive signals.
Given our initial findings using Branch-site methods, we explored alternative signatures of divergence in selective constraint using the Clade model C (CmC) approach [ 20, 21, 66].
