Topological pure gauge theories in lower dimension are presented as a warm-up.
Compared with the existing results, the observer has a wider application range and a lower dimension.
Another example is data compression when original data is transformed into a space of lower dimension.
In practice, datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect the structure of tasks or latent variables.
In particular, data representation in a lower dimension is needed in applications, where information comes from multiple high dimensional sources.
Combined with the Galerkin projection, POD is a tool for generation of reduced models of lower dimension.
MEANDS relaxes several restrictions of predecessor algorithms, such as the size of sub-populations and the need for weights in the lower dimension MOPs.
The computation of the remaining states of the system requires matrices of lower dimension and thus the method is less susceptible to ill-conditioning.
We show that the components design can be conveniently cast as a structured real eigenvalue assignment with significantly lower dimension than the state size of the Marx circuit.
This approach reduces a complex set of data to a lower dimension, by representing the behavior of the structure through the few variables.
