Bottom-up modeling

CEMSIM is a bottom-up model of the cement industry, developed by the IPTS (Szabo et al., 2003, 2006). It pays particular attention to fuel and technology dynamics. Seven technologies are included, characterized by energy, material and labour consumptions, an investment cost, and a set of retrofitting options. 

In this contribution we discuss mm based on pp chromophores, a very interesting class of molecules for applications in molecular photonics and electronics. Push-pull chromophores are both polar and polarizable and this makes the role of intermolecular interactions particularly important. The toy model we propose for clusters of pp chromophores neglects intermolecular overlap, just accounting for classical electrostatic intermolecular interactions, and describes each pp chromophore based on a two state model. The two-state model for pp chromophores has been discussed and validated via an extensive comparison with the spectroscopic properties of several dyes in solution [74, 75, 90], The emerging picture is safe and led to the definition of a reliable set of molecular parameters for selected dyes. This analysis then offers valuable information to be inserted into models for clusters of interacting chromophores, in a the bottom-up modeling strategy that was nicely exemplified in Ref. [90]. 

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. 

Structure Component level Functional links (delays, reoalr) Non linear, time dynamics of system behavior Integrate physical Bottom-up modeling of interdependencies Cascade representation Reliability analysis Vulnarabllity analysis System alannina Resilience analysis Operations eatlmlzatlon... 

Approaches that can embed physical laws are promising, i.e. ABM has capability of representing the complexity of Cl through bottom-up modeling of its elements and does not explicitly assume interdependencies. ABM can be integrated with many other modeling/simulation techniques including... 

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,... 

Three statistical approaches described so far (CCK, LCA, and MA) rely on statistical models. They make certain assumptions about the latent structure, which allows them to define models and then evaluate these models with the data. CA is radically different it goes bottom-up from the data and does not make any structural assumptions whatsoever. CA is inductive and exploratory in nature. Is this advantageous We will come back to this question, but first let us get better acquainted with the methodology. 

The procedures are grouped in two general classes inferential and descriptive. These labels are not an established convention, but rather, are used to highlight the fundamental difference between the completely atheoretical approach of CA and the model-guided approach of the other methods. Among the inferential methods, CCK is based on the strictest model, LCA makes fewer assumptions, and MA uses a bottom-up, fit-oriented approach. 

The bottom-up approach, which develops detailed plant simulation and optimization models, optimizes them, and translates the results from the simulations and optimization into practical operating heuristics. This approach often leads to large models with many variables and equations that are difficult to solve quickly using rigorous optimization algorithms. 

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 economic values in the objective function are treated and structured from an operations research perspective as variables calculated bottom up considering underlying volume decision variables. These result variables are integrated in the model to make the objective function more readable and easier to communicate to stakeholders such as planners, top-management, marketing and/or controlling. 

The values for metabolite concentrations are needed at different occasions for modeling metabolism (i) as an additional data source to validate kinetic models that are constructed in a bottom-up approach, (ii) as starting point for steady-state search algorithms, (iii) as additional experimental data for... 

The usual design philosophy for this type of methods is bottom-up one starts with an approximate model, compares results with experiments, analyzes the deviations, and uses them to determine empirical corrections and/or additional terms to be added to the model, after which the cycle is repeated if desired. 

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. 

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