GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911196763914240 |
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| author | Mu, Haoxuan Dylan Tang, Mingjian Gao, Wei Chen, Wei "Wayne" |
| author_facet | Mu, Haoxuan Dylan Tang, Mingjian Gao, Wei Chen, Wei "Wayne" |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05641 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses Mu, Haoxuan Dylan Tang, Mingjian Gao, Wei Chen, Wei "Wayne" Computational Engineering, Finance, and Science 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. |
| title | GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.05641 |