Your English writing platform
Discover LudwigExact(13)
An uncertainty quantification scheme is constructed based on generalized Polynomial Chaos (PC) representations.
An uncertainty quantification scheme is developed for the simulation of stochastic thermofluid processes.
Rather than propose an alternative quantification scheme, we argue that fundamentally there is no need to quantify robustness as an independent figure of merit.
The purpose of this paper is to present a low-cost impact detection and quantification scheme for thin plates or shells giving the whole history of the structure solicitation.
This paper also presents an uncertainty quantification scheme using commercial finite element software (ANSYS) and thereby comparative results of stochastic natural frequencies are furnished for UQ using GHDMR approach and ANSYS.
The uncertainty quantification scheme is adapted from the spectral stochastic finite element method (SSFEM), which is based on regarding uncertainty as generating a new dimension and the solution as being dependent on this dimension.
Similar(47)
Relationships with other modern uncertainty quantification schemes and promising research directions are discussed.
Therefore, this study was performed to identify the potential hazardous states for network communication between GCs and LCs and to develop quantification schemes for various network failure causes.
Uncertainty quantification schemes based on stochastic Galerkin projections, with global or local basis functions, and also stochastic collocation methods in their conventional form, suffer from the so called curse of dimensionality: the associated computational cost grows exponentially as a function of the number of random variables defining the underlying probability space of the problem.
Two quantification schemes are investigated in this paper.
Finally, we find, in this limited data set, that the novel statistical image analysis scheme appears useful for prediction of chemotherapy response during the course of therapy, especially when compared to more traditional DOT quantification schemes.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com