Reliability-based Topology Optimization using Large Deviation Theory
Reliability-based topology optimization (RBTO) requires repeated estimation of small failure probabilities and their gradients, making conventional nested Monte Carlo approaches computationally prohibitive for large scale structural problems. We propose an RBTO framework that integrates large deviation theory~(LDT) with stochastic gradient descent~(SGD) to address this challenge. LDT provides closed-form exponential rate estimates of rare event probabilities, enabling accurate gradient computation without parametric assumptions on the failure density and without evaluating a full nested reliability loop at every iteration. These LDT-based gradient estimates are used directly to drive a mini batch SGD update of the design variables using only a few random samples per iteration. The framework is validated on three benchmarks, namely, a two-dimentional (2D) simply supported rectangular beam, a 2D L-shaped beam, and a three-dimentional (3D) cantilever beam, under both compliance-based and stress-based failure criteria. Across these examples, the RBTO designs achieve failure probabilities lower than their robust topology optimization counterparts, demonstrating that optimizing for performance under uncertainty alone does not guarantee the satisfaction of explicit reliability constraints. Therefore, the proposed framework offers a computationally efficient route to reliable structural design under uncertainty, with direct relevance to safety-critical engineering applications.