Reliability-based topology optimization using stochastic gradients

This paper addresses the computational challenges in reliability-based topology optimization (RBTO) of structures associated with the estimation of statistics of the objective and constraints using standard sampling methods. The aim is to overcome the accuracy issues of traditional methods that rely on approximating the limit-state function. Herein, we present a stochastic gradient-based approach, where we estimate the probability of failure at every few optimization iterations using an efficient sampling strategy. To estimate the gradients of the failure probability with respect to the design parameters, we apply Bayes' rule wherein we assume a parametric exponential model for the probability density function of the design parameters conditioned on the failure. The design parameters and the parameters of this probability density function are updated using a stochastic gradient descent approach requiring only a small, e.g., O(1), number of random samples per iteration, thus, leading to considerable reduction of the computational cost as compared to standard RBTO techniques. We illustrate the proposed approach with a benchmark example that has an analytical solution as well as two widely used problems in structural topology optimization. These examples illustrate the efficacy of the approach in producing reliable designs.

Automated summary

This paper presents a stochastic gradient-based approach to address the computational challenges in reliability-based topology optimization of structures. By using Bayes' rule and a parametric exponential model, the gradients of the failure probability can be efficiently estimated, requiring only a small number of random samples per iteration.