# Computational Acceleration of Topology Optimization Using Deep Learning
## 7 Conclusions
For a laptop with Intel Core i7-4510 @2.00 Ghz with up to 2.60 Ghz, the time for one iteration is around 0.88 s. Thus, optimized topology sample generation time can be in the range of tens of seconds to thousands of seconds, respectively. The binary results for the SIMP model estimation were obtained using \"ToPy--Topology optimization with Python\" code [26]. To obtain better results, a higher number of elements is required, which leads to an increase in sample generation time. In general, it requires a minimum of a few hundred of seconds to generate a fine and detailed optimized topology. According to trained deep learning models, the testing time for the suggested models is less than 1 msec, which is incredibly faster than SIMP model generation. It is worth mentioning that deep learning (DL) requires some time for data preparation and training. Although, optimally designed and trained DL models definitely give advantages in obtaining optimized layouts compared to conventional TO methods. Thus, improved ML models provide reduced training times and better accuracies for TO problems; therefore, they can be useful for the acceleration of computational time. In the work, we have clearly showed that these new models are highly efficient, robust and reliable compared to their predecessors, TopologyGAN [18] and SILONET [19]. Conceptualization, S.R. and B.A. ; Methodology, S.R. and B.A. ; Software, J.R.; Validation, M.N. ; Formal analysis, Y.M. and M.N. ; Investigation, J.R., S.R., B.A. and Y.M. ; Resources, Y.M. ; Data curation, M.N. ; Writing--original draft, J.R., S.R., B.A. and Y.M. ; Visualization, M.N. ; Supervision, S.R. ; Project administration, B.A. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment \#** & **Depth** & **Min. Filters** & **Accuracy** \\ \hline
1 & 3 & 32 & 83.63 \\ \hline
2 & 6 & 32 & 90.07 \\ \hline
3 & 3 & 64 & 84.32 \\ \hline
4 & 6 & 64 & 89.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Binary accuracy score for the Res-U-Net depends on model parameters on the Dataset #2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & \
\begin{tabular}{c} **Data** \\ **Distribution** \\ \end{tabular} & **Depth** & **Filters on Each Layer** &
\begin{tabular}{c} **Accuracy after 5 th** \\ **Iteration of SIMP** \\ \end{tabular} \\ \hline
1 & Uniform [1–100] & 3 & 16, 32, 64, 64, 32, 16 & 93.59 \\ \hline
2 & Poisson (5) & 3 & 16, 32, 64, 64, 32, 16 & 94.03 \\ \hline
3 & Poisson (10) & 3 & 16, 32, 64, 64, 32, 16 & 94.06 \\ \hline
4 & Poisson (30) & 3 & 16, 32, 64, 64, 32, 16 & 93.88 \\ \hline
5 & Uniform [1–100] & 4 & 16, 32, 64, 128, 128, 64, 32, 16 & 93.78 \\ \hline
6 & Poisson (5) & 4 & 16, 32, 64, 128, 128, 64, 32, 16 & 94.08 \\ \hline
7 & Poisson (10) & 4 & 16, 32, 64, 128, 128, 64, 32, 16 & 93.98 \\ \hline
8 & Poisson (30) & 4 & 16, 32, 64, 128, 128, 64, 32, 16 & 93.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Binary accuracy score for the Res-U-Net depends on model parameters on the Dataset #1. **Funding:** This work has been supported by the research programme of the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Young Researcher's Grant No. AP08856141). **Conflicts of Interest:** The authors declare no conflict of interest.