Modeling data-driven

THE SPECIFICATION MODEL DATA-DRIVEN EXECUTION OF INSTRUCTION STREAMS... 

First-principle modelling Data-driven modeling... 

Whereas a model-driven method imposes a rigid classification scheme onto a set of reactions, the data-driven methods try to derive a classification from the data presented. 

There are two fundamental approaches to automatic reaction classification model-driven and data-driven methods. 

Teegavarapu RSV, Chandramouli V (2005) Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J Hydrol 312 191-206... 

Despite the broad definition of chemometrics, the most important part of it is the application of multivariate data analysis to chemistry-relevant data. Chemistry deals with compounds, their properties, and their transformations into other compounds. Major tasks of chemists are the analysis of complex mixtures, the synthesis of compounds with desired properties, and the construction and operation of chemical technological plants. However, chemical/physical systems of practical interest are often very complicated and cannot be described sufficiently by theory. Actually, a typical chemometrics approach is not based on first principles—that means scientific laws and mles of nature—but is data driven. Multivariate statistical data analysis is a powerful tool for analyzing and structuring data sets that have been obtained from such systems, and for making empirical mathematical models that are for instance capable to predict the values of important properties not directly measurable (Figure 1.1). 

The empirical modeling element indicates an increased emphasis on data-driven rather than theory-driven modeling of data. This is not to say that appropriate theories and prior chemical knowledge are ignored in chemometrics, only that they are not relied upon completely to model the data. In fact, when one builds a chemometric calibration model for a process analyzer, one is likely using prior knowledge or theoretical relations of some sort regarding the chemistry of the sample or the physics of the analyzer. One example... 

The randomisation test proposed by Wiklund et al. [34] assesses the statistical significance of each individual component that enters the model. This had been studied previously, e.g. using a t- or F-test (for instance, Wold s criterion seen above), but they are all based on unrealistic assumptions about the data, e.g. the absence of spectral noise see [34] for more advanced explanations and examples. A pragmatic data-driven approach is therefore called for and it has been studied in some detail recently [34,40]. We have included it here because it is simple, fairly intuitive and fast and it seems promising for many applications. 

The reductionist approach to science has been extremely successful for several centuries. Its goal was the postulation of a hypothesis, later of a model, and ultimately of a theory, from experimental data (induction) or the postulation of a model with subsequent experimental verification (deduction). Parallel to the availability of a hyper-exponentially growing amount of data, a complementary approach is taking shape, termed a data-driven or systems approach. 

The data-driven approach does not render the model-driven approach obsolete or superfluous. In fact, data-driven approaches have to rely, and will do so for the foreseeable future, on model-driven advances in single fields of science (Aebersold, 2000). In addition, without knowledge gained with reductionist approaches interpretation of data with the systems approach is not possible. Novel tools such as pathway analysis and pattern recognition, and novel kinds of research questions such as the investigation of hypotheses based on the interaction of several system components, become possible with the systems approach. 

K. Yamuna Rani and S.C. Patwardhan. Data-driven model based control of a multi-product... 

Early approaches to fault diagnosis were often based on the so-called physical redundancy [11], i.e., the duplication of sensors, actuators, computers, and softwares to measure and/or control a variable. Typically, a voting scheme is applied to the redundant system to detect and isolate a fault. The physical redundant methods are very reliable, but they need extra equipment and extra maintenance costs. Thus, in the last years, researchers focused their attention on techniques not requiring extra equipment. These techniques can be classified into two general categories, model-free data-driven approaches and model-based approaches. 

Model-free data-driven approaches do not require a model of the monitored process, but only a good database of historical data collected in normal operating conditions. This class of approaches includes both statistical and knowledge-based methods [49],... 

Model background Purely data-driven Biology/physiology-driven and data-driven... 

As described in Section 17.2, the model building is mainly data-driven and drug-dependent, model parameters and their variability are estimated based on the data available. 

Overall, these models are closer to the empirical PD models, but major elements of the biological system are implemented. Semi-mechanistic models are mostly developed using the population approach and consequently they are data-driven and parameters are estimated from the data available. Parameters which cannot be estimated might be either fixed to biologically meaningful values or they are explored by other studies, including in vitro or preclinical in vivo studies. Overall, the number of parameters is still small, compared with mechanistic PD models and the majority of the parameters are estimated. 

The Input Translator is completely table driven. This means that all of the information needed to process input statements (such as names of keywords, default values of data items, etc.) is stored in tables in a file called the System Definition File. Therefore, it is easy to add keywords or change defaults by changing entries in the System Definition File. In addition to the Input Language tables, almost any "changeable" information related to Input Translation is stored in the System Definition File. This includes unit conversion tables, attribute descriptions, physical property option models, data structure, unit operation model data, and stream requirements, etc. Thus it is easy to add new system parameters without changing any code in the Input Translator. 

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