Date of Award

Spring 1-1-2019

Document Type


Degree Name

Master of Science (MS)


Aerospace Engineering Sciences

First Advisor

Kurt Maute

Second Advisor

Alireza Doostan

Third Advisor

John A. Evans


The goal of this work is to investigate the ways in which the capabilities of machine learning algorithms, specifically those of neural networks, can be leveraged to enhance the performance of design optimization algorithms -- specifically those of topology optimization.

A recent boom of interest in design optimization has occurred, coinciding with the arrival and development of advanced manufacturing techniques (such as 3D printing and additive manufacturing) which are compatible with the designs generated by these algorithms. Neural networks have seen an even larger boom in interest and development for their ability to act as ``universal function generators;" in other words, for their ability to learn highly non-linear functions that approximate the behavior of extremely complex systems. Merging design optimization algorithms with the capabilities of neural networks poses several distinct possibilities: drastically reducing optimization time by predicting solution convergence; up-scaling solution resolution using Generative Adversarial Networks (GAN's); predicting solutions with no iteration; predicting and recognizing features in the optimized solution, just to name a few.

In this thesis, three neural network architectures are tested for their ability to act as solution convergence predictors of a density-based topology optimization solver. The problem is posed as an image segmentation problem, and the neural networks are all trained on a 40,000 example training set with each example containing 100 iterations from the open source optimization solver Topy, (a data set created by Sosnovik et al (2017)). The third network developed and tested is a novel hybrid network -- an inception encoder-decoder network -- which is found to outperform the other networks on the prediction task at hand.