A Direct Inverse Design Framework Using Bayesian Machine Learning

Abstract

Inverse design is a concept where one can design a structure via a property-first approach, where the properties of a system act as precursor information to guide the search for a viable design. This concept is classified as one of two: indirect or direct. While the former is no different than a forward-based approach using traditional optimization, a direct inverse design framework attempts to invert the process and directly map the desired properties to a suitable design candidate, which is unconventional. As such, the design process can be positively radicalized, with regards to the required computational resources when designing multiple structures.

In this work, the use of Bayesian machine learning, more specifically Bayesian optimization using Gaussian process regression, is employed to construct a direct inverse design framework using the design of mass-optimized fixed-wing aircraft ribs against buckling as the validation case study in 1, 6, and 10 dimensions. The results are compared to an indirect approach using the same algorithms and demonstrate that barring certain limitations, which are not inherent flaws of the direct approach, the framework designed not only demonstrates sound potential in inverting the forward map, but outperforms the indirect approach when designing multiple structures.

This research equally provides a stepping stone towards future research possibilities in the same field, all culminating in the improvement of the multi-disciplinary design process.