Top-down modeling

Projected emissions from manufacturing industry were estimated from a combination of a bottom-up survey by ICF/BOC and top-down modelling of energy demand by ESRI. 

With top-down modeling, you can create surfiices or three-dimensional solid objects using area and volume primitives. Primidves are simple geometric shapes. Two-dimensional primitives indude rectai es, drdes, and polygons, and three-dimensional volume primitives indude blocks, prisms, cylinders, cones, and spheres, as shown in Figure 16.24. 

One such structured methodology is known as the top-down approach. Such an approach can be graphically illustrated in atop-down model as shown in Fig. 19.54. Using a top-down approach as illustrated in the top-down model is relatively straightforward. 

Given these requirements as determined by the upper layers of the top-down model, the next job is to determine the requirements of the network that will possess the capability to deliver this data in a timely, cost-effective manner. Details on the determination of these requirements comprise the remainder of this section on network communications. These network performance criteria could be referred to as what the implemented network must do in order to meet the business objectives outlined at the outset of this top-down analysis. These requirements are also sometimes referred to as the logical network design. 

Overall, the relationship between the layers of the top-down model could be described as follows analysis at upper layers produces requirements that are passed down to lower layers while solutions meeting these requirements are passed back to upper layers. If this relationship among layers holds true throughout the business oriented network analysis, then the implemented technology (bottom layer) should meet the initially outlined business objectives (top layer). 

A Corrosion Index for Pipeline Risk Evaluation The third example of top down models is the pipeline risk assessment methodology that is described in much detail in the 2nd edition of a popular book on pipeline risk management [20]. Since this book was published, many systems... 

Approaches synergistically combining both top-down and bottom-up modeling viewpoints should be further developed. Macroscopic equations in top-down models should be written in terms of parameters with values calculated from lower-scale simulations. Implementation of such parameters into the macroscopic model should be done including empirical errors. Methodological evaluation of these parameters should be done systematically for instance, coarse models should be developed first, with parameter sensitivity studies guiding further calculations at lower scales. 

Explicitly model of the independencies vs. simulating the behavior of the Cl interconnected within the SoS and assess the strength of interdependencies as an emerging system property. The former is a top-down model which assumes the existence of interdependencies. The latter is bottom-up system description which maps the functional relations among components in different systems. ABM can be regarded as the bottom-up approach CN, SD, and DCST can represent system functionality as bottom-up approaches PN, BN and IIM require the identification of system dependencies and are top-down approaches. 

The dynamic,s underlying EINSTein is patterned after mobile CA rules, and are somewhat reminiscent of Braitenberg s Vehicles [brait84]. Specifically, EINSTein takes a artificial-life-like bottom-up, synthesist approach to the modeling of combat, rather than the more traditional top-down, or reductionist approach,... 

A standard continuous-time job-shop scheduling formulation [3] can be used to model the basic aspects of the production decisions, such as sequencing and assignment of jobs. Here, the key of the mathematical solution is to capture the durations of each processing step and to relate it to the amounts of material. Therefore, only a top-down approach will be presented to illustrate some main principles of the model. 

Build2-3. Refine it to specify the system by building its local type model of the domain the retrieval mapping is defined top-down. 

A project produces a subset of models and diagrams that describe the design. For some projects, the order in which these are produced will be mostly top-down for others, more bottom-up. In almost all cases there are multiple development tasks that can proceed in parallel depending on project resources and constraints. In all cases, the relationships among the artifacts are the same, and the most important initial methodology question for any project to answer is... 

The PACE-T(H2) model analyses the world economy based on the GTAPinGAMS structure (Rutherford, 1998). It incorporates top-down benchmark data in the form of social accounting matrices to which the model is calibrated. The main data source for the calibration of national and international commodity flows is the GTAP5 database (GTAP, 2002). The trade shares of hydrogen and conventional cars are taken from the sector Motor vehicles and parts of the GTAP database. 

Several approaches are utilized to study systems biology. The bottom-up approach starts from the molecular level, the omics, to identify and evaluate the genomic and proteomic basis of diseases. The top-down approach attempts to integrate human physiology and diseases to provide models to understand disease pathways at organ levels. 

Starting from the model and the stock of hypotheses, it was possible in the deductive top down movement , on the other hand, to make more accurate enquiries regarding the particular innovation processes in the case studies. As stated, both these movements must only be differentiated as ideal types. 

A closer examination of the case studies reveals the extreme complexity and inter-hnked nature of the processes in an innovative system. Determining, which were the decisive factors that were manifest by a particular example of substitution, tended to be irresolvable in view of this complexity. A top-down analysis of the systems view of the simple model assists in orientation. In addition, some phenomena that are important for iimovation processes can only be revealed from a systems view, e.g. system inertia and system ambience , which is frequently referred to as the innovation chmate . Decisively, phenomena such as emergence are only discemable at a systems level. Emergence is of central importance for the comprehension of innovation processes, where development of a new element is the core feature. Emergence means that a novel, impredicted and usually complex feature is produced in the system (or by the system) which no individual contributor had planned or could conceivably plan. In most cases, new elements can neither be commanded externally nor can they be negotiated in a discourse between the participants from their estabhshed interests. Creativity is required here. 

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