Optimum design of nonlinear structures via deep neural network-based parameterization framework. (English) Zbl 1519.74054
The authors solve the sizing optimization problem for truss structures. The mathematical model has the form
\begin{align*}
&\text{Minimize}\ W(\mathbf{A})= \sum_{k=1}^{n_g} A_k\sum_{i=1}^{m_k} \rho_iL_i,\\
&\text{subjected to}\ g_j(\mathbf{A})=\frac{\Delta_j}{|\Delta_j|}-1 \le 0,\ j=1,2,...p,\ A_k^{\mathrm{law}}\le A_k \le A_k^{up},\ k=1,...,n_g.
\end{align*}
The problem is rewritten in a form
\[
\text{Minimize}\ \mathcal{L}(\mathbf{A})=(1+\varepsilon_1 c)^{\varepsilon_2}W(\mathbf{A}),\ c=\sum_{j=1}^p\max (0,g_j(\mathbf{A}),
\]
which is solved using a deep neural network.
Reviewer: Igor Bock (Bratislava)
MSC:
| 74P05 | Compliance or weight optimization in solid mechanics |
| 74S99 | Numerical and other methods in solid mechanics |
| 74K10 | Rods (beams, columns, shafts, arches, rings, etc.) |
| 68T07 | Artificial neural networks and deep learning |
Keywords:
weight optimization; hyperparameter optimization; Bayesian optimization; geometrically nonlinear space truss; machine learningReferences:
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