This thesis explores novel parameterization concepts for large scale topology optimization that enables the use of evolutionary algorithms in large-scale structural design. Specifically, two novel parameterization concepts based on generative algorithms and Boolean random networks are proposed that facilitate systematic exploration of the design space while limiting the number of design variabl...

The authors develop a parameterization to relate the local inventory of gas hydrate (in g/cmˆ2) to relevant input parameters (like water depth). These parameterizations are tuned to one-dimensional numerical models, which circumvents to the need to run numerical models with gridded data. Another approach is to use the numerical models to construct "look-up" tables for use with gridded data. The...

Type parameterization is the ability to defrne a type in terms of another, unspecifled, type. Versions of the parameterized type may then be created for several particular parameter types. A language supporting type parameterization allows specification ofgeneral container types such as list, vector, and associative array where the specific type of the elements is left as a parameter. Thus, a p...

The conventional exponential family random graph model (ERGM) parameterization leads to a baseline density that is constant in graph order (i.e., number of nodes); this is potentially problematic when modeling multiple networks of varying order. Prior work has suggested a simple alternative that results in constant expected mean degree. Here, we extend this approach by suggesting another altern...

this paper investigates a procedure for identifying all efficient hyperplanes of production possibility set (pps). this procedure is based on a method which recommended by pekka j. korhonen[8]. he offered using of lexicographic parametric programming method for recognizing all efficient units in data envelopment analysis (dea). in this paper we can find efficient hyperplanes, via using the para...

Progress in statistical computation often leads to advances in statistical modeling. For example, it is surprisingly common that an existing model is reparameterized, solely for computational purposes, but then this new conŽ guration motivates a new family of models that is useful in applied statistics. One reason why this phenomenon may not have been noticed in statistics is that reparameteriz...