Kavli Affiliate: Wei Gao
| First 5 Authors: Wei Zhang, Mingjian Tang, Haoxuan Mu, Xingzi Yang, Xiaowei Zeng
| Summary:
In many biological materials such as nacre and bone, the material structure
consists of hard grains and soft interfaces, with the interfaces playing a
significant role in the material’s mechanical behavior. This type of structures
has been utilized in the design of various bio-inspired composite materials.
Such applications often require the materials to exhibit a specified nonlinear
stress-strain relationship. A key challenge lies in identifying appropriate
interface properties from an infinite search space to achieve a given target
stress-strain curve. This study introduces a Bayesian optimization (BO)
framework specifically tailored for the inverse design of interfaces in
bio-inspired composites. As a notable advantage, this method is capable of
expanding the design space, allowing the discovery of optimal solutions even
when the target curve deviates significantly from the initial dataset.
Furthermore, our results show that BO can identify distinct interface designs
that produce similar target stress-strain responses, yet differ in their
deformation and failure mechanisms. These findings highlight the potential of
the proposed BO framework to address a wide range of inverse design challenges
in nonlinear mechanics problems.
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