Kavli Affiliate: Wei Gao
| First 5 Authors: Haoxuan Dylan Mu, Haoxuan Dylan Mu, , ,
| Summary:
Inverse design problems are pervasive in engineering, particularly when
dealing with nonlinear system responses, such as in mechanical behavior or
spectral analysis. The inherent intractability, non-existence, or
non-uniqueness of their solutions, and the need for swift exploration of the
solution space necessitate the adoption of machine learning and data-driven
approaches, such as deep generative models. Here, we show that both deep
generative model-based and optimization-based methods can yield unreliable
solutions or incomplete coverage of the solution space. To address this, we
propose the Generative and Uncertainty-informed Inverse Design (GUIDe)
framework, leveraging probabilistic machine learning, statistical inference,
and Markov chain Monte Carlo sampling to generate designs with targeted
nonlinear behaviors. Unlike inverse models that directly map response to
design, i.e., response $mapsto$ design, we employ a design $mapsto$ response
strategy: a forward model that predicts each design’s nonlinear functional
response allows GUIDe to evaluate the confidence that a design will meet the
target, conditioned on a target response with a user-specified tolerance level.
Then, solutions are generated by sampling the solution space based on the
confidence. We validate the method by designing the interface properties for
nacre-inspired composites to achieve target stress-strain responses. Results
show that GUIDe enables the discovery of diverse feasible solutions, including
those outside the training data range, even for out-of-distribution targets.
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