RID-Noise: Towards Robust Inverse Design under Noisy Environments

• URL: http://arxiv.org/abs/2112.03912v1
• Date: Tue, 7 Dec 2021 06:32:27 GMT
• Title: RID-Noise: Towards Robust Inverse Design under Noisy Environments
• Authors: Jia-Qi Yang, Ke-Bin Fan, Hao Ma, De-Chuan Zhan
• Abstract summary: We propose Robust Inverse Design under Noise (RID-Noise) to train a conditional invertible neural network (cINN) We estimate the robustness of a design parameter by its predictability, measured by the prediction error of a forward neural network. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data.
• Score: 30.58112077143225
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has been widely implemented in the industry for product quality control. However, classic robust design requires a lot of evaluations for a single design target, while the results of these evaluations could not be reused for a new target. To achieve a data-efficient robust design, we propose Robust Inverse Design under Noise (RID-Noise), which can utilize existing noisy data to train a conditional invertible neural network (cINN). Specifically, we estimate the robustness of a design parameter by its predictability, measured by the prediction error of a forward neural network. We also define a sample-wise weight, which can be used in the maximum weighted likelihood estimation of an inverse model based on a cINN. With the visual results from experiments, we clearly justify how RID-Noise works by learning the distribution and robustness from data. Further experiments on several real-world benchmark tasks with noises confirm that our method is more effective than other state-of-the-art inverse design methods. Code and supplementary is publicly available at https://github.com/ThyrixYang/rid-noise-aaai22

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