# Design Technology and AI-Based Decision Making Model for Digital Twin Engineering
## 2 Literature Review
Each of these methods imposes special requirements on the necessary computing resources. For example, data mining methods require large-scale storage with high bandwidth for collecting and accessing analytical data, as well as a high scalability for the computing system for processing them; machine learning methods require nodes with installed graphics accelerators. Models based on data mining are used to discover knowledge in the data previously unknown, non-trivial, or practically useful and open to interpretation, necessary for making strategically important decisions. Artificial intelligence and machine learning are effectively used in digital warehouse forecasting. The use of these methods makes it possible to achieve a level of predictive accuracy higher than that based on traditional simulation methods [13]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Feature**} & \multicolumn{3}{c}{**Modeling Approach**} \\ \cline{2-4}  & **Mathematical Modeling** & **Data-Based Modeling** & **Hybrid Modeling** \\ \hline \multirow{3}{*}{Predictive capability} & Prediction in wide ranges of parameter values described by the model & Difficulty in predicting rare events as well as in conditions of incomplete data and noisy information, as well as outside of training samples & High predictive ability within regular/ emergency situations \\  & Decision making is based & Decision making is based on the & Decision making is based on the & \\ \multirow{3}{*}{\
\begin{tabular}{c} Priority approach \\ to decision making and management \\ \end{tabular} } & on an analysis of the overall & analysis of monitoring data and & Solving both direct and inverse control problems \\  & performance (efficiency) of the system. & diagnostics. Management decisions & inverse control problems \\  & Management decisions based on the & are based on prediction and the & solving of direct problems \\  & solution of inverse problems & solving of direct problems & \\ \multirow{2}{*}{
\begin{tabular}{c} Type of \\ control system \\ \end{tabular} } & Deviation control, & Deviation control, & into account weak \\  & adaptive control & adaptive control & environmental signals; \\  & All stages & Exploitation & Growth, stability \\  & & & Mathematical Modeling + \\ \multirow{2}{*}{Operation scheme} & Numerical simulation + sensors\(\rightarrow\) & Sensors + IIoT platform\(\rightarrow\) & Sensors\(\rightarrow\) \\  & Data acquisition\(\rightarrow\)IIoT platform & data collection\(\rightarrow\)data analytics & Data Acquisition\(\rightarrow\) \\  & & & IIoT-platform\(\rightarrow\)analytics \\ Tools & Matlab Simulink, ANSYS, AnyLogic, & R, Python, Statictica. & Interdisciplinary \\  & Ithink etc. & GPSS etc. & Platforms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The use of \"big data\" has its limitations, associated with incomplete or noisy data, and difficulties in predicting rare events. Extrapolation methods do not allow for such predictions. For some products, sensors are expensive to install and maintain, sensors are prone to errors, failures can give incorrect readings, and the results can overwhelm users with redundant information. Without a structural (mathematical, physical) model, it is difficult to determine the areas of technical devices where it is advisable to locate sensors. Collecting raw data from sensors is only part of the modeling process. At the stage when the inverse problem appears, that is, when it is necessary to restore the picture of what is happening on the basis of data received from sensors without a mathematical model, this problem turns out to be intractable, since most of the collected data are unusable \"garbage\", of which it is very difficult to select a meaningful part that adequately describes the object (process). At the same time, the mathematical modeling of objects (processes) in combination with data-based models provides more opportunities for forecasting than models based only on machine learning technologies.