Stochastic particle-resolved methods are a useful way to compute the time evolution of the multi-dimensional size distribution of atmospheric aerosol particles.
So it was a multi dimensional challenge.
Poverty is multi dimensional.
The developed scheme is subsequently applied to solve multi-dimensional PBEs with size-dependent growth rate by drawing an analogy between the advection equation and the PBE.
The main advantage of the method over traditional population balance solvers is its capability to treat multi-dimensional (e.g. size, crystal morphology, chemical properties, etc).
On the contrary, the sparsity penalty forces sMBPLS to focus on 'local' (i.e. across a small subsets of variables and samples) peaks in the covariance, which correspond to (multi-dimensional) modules of relatively small size.
Penalized maximum likelihood estimation provides the means to 'make the most' of limited and precious data and facilitates more stable estimation for multi-dimensional analyses, even when samples sizes are larger.
The discrete levels of variables are established in such a way that the size of the multi-dimensional experimental space was in the range of 200 000 theoretical experiments.
For instance, in contrast to industry-driven Big Data "V's" definitions, Dr. Ivo Dinov for his research scope listed another data's multi-dimensional characteristics [30] such as data size, incompleteness, incongruency, complex representation, multiscale nature and heterogeneity of its sources [31, 32].
In 2007, Zha et al. employed such a multi-dimensional microscope to determine the shape, size and distribution of solid particles in synovial fluids.
Stochastic particle-resolved methods have proven useful for simulating multi-dimensional systems such as composition-resolved aerosol size distributions.
