Can I read Inverse design of spinodoid structures using Bayesian optimization on EtoBox?

Inverse design of spinodoid structures using Bayesian optimization by Raßloff, Alexander; Seibert, Paul; Kalina, Karl A.; Kästner, Markus is a scholarly article available to read on EtoBox.

What is Inverse design of spinodoid structures using Bayesian optimization about?

Tailoring materials to achieve a desired behavior in specific applications is of significant scientific and industrial interest as design of materials is a key driver to innovation. Overcoming the rather slow and expertise-bound traditional forward approaches of trial and error, inverse design is attracting substantial attention. Targeting a property, the design model proposes a candidate structure with the desired property. This concept can be particularly well applied to the field of architected materials as their structures can be directly tuned. The bone-like spinodoid materials are a specific class of architected materials. They are of considerable interest thanks to their non-periodicity, smoothness, and low-dimensional statistical description. Previous work successfully employed machine learning (ML) models for inverse design. The amount of data necessary for most ML approaches poses a severe obstacle for broader application, especially in the context of inelasticity. That is why we propose an inverse-design approach based on Bayesian optimization to operate in the small-data regime. Necessitating substantially less data, a small initial data set is iteratively augmented by

Author

Raßloff, Alexander; Seibert, Paul; Kalina, Karl A.; Kästner, Markus

Published

2024

Language

EN