Kavli Affiliate: Wei Gao
| First 5 Authors: Haoxuan Dylan Mu, Haoxuan Dylan Mu, , ,
| Summary:
Inverse design is a common yet challenging engineering problem, particularly
for nonlinear functional responses such as mechanical behavior or spectral
analysis. Deep generative models are motivated by intractability, non-existence
or non-uniqueness of solutions, and the need for rapid solution-space
exploration. In this study, we show that deep generative model-based and
optimization-based approaches can provide incomplete solutions or hallucinate
given out-of-distribution targets. To address this, we propose the Generative
and Uncertainty-informed Inverse Design (GUIDe) framework, which leverages
probabilistic machine learning, statistical inference, and Markov chain Monte
Carlo to generate designs with targeted nonlinear behaviors. Instead of inverse
mappings, i.e., response $mapsto$ design, GUIDe adopts design $mapsto$
response: a forward model predicts each design’s nonlinear functional response
and evaluates the confidence under a user-specified tolerance. Sampling the
solution space by this confidence yields diverse feasible designs. Our
validation on nacre-inspired materials finds solutions beyond the training
range, even under out-of-distribution targets.
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