GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins

• URL: http://arxiv.org/abs/2106.14655v1
• Date: Thu, 24 Jun 2021 06:40:35 GMT
• Title: GAN-MDF: A Method for Multi-fidelity Data Fusion in Digital Twins
• Authors: Lixue Liu, Chao Zhang, Dacheng Tao
• Abstract summary: Internet of Things (IoT) collects real-time data of physical systems, such as smart factory, intelligent robot and healtcare system. High-fidelity (HF) responses describe the system of interest accurately but are computed costly. Low-fidelity (LF) responses have a low computational cost but could not meet the required accuracy. We propose a novel generative adversarial network for MDF in digital twins (GAN-MDF)
• Score: 82.71367011801242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Internet of Things (IoT) collects real-time data of physical systems, such as smart factory, intelligent robot and healtcare system, and provide necessary support for digital twins. Depending on the quality and accuracy, these multi-source data are divided into different fidelity levels. High-fidelity (HF) responses describe the system of interest accurately but are computed costly. In contrast, low-fidelity (LF) responses have a low computational cost but could not meet the required accuracy. Multi-fidelity data fusion (MDF) methods aims to use massive LF samples and small amounts of HF samples to develop an accurate and efficient model for describing the system with a reasonable computation burden. In this paper, we propose a novel generative adversarial network for MDF in digital twins (GAN-MDF). The generator of GAN-MDF is composed of two sub-networks: one extracts the LF features from an input; and the other integrates the input and the extracted LF features to form the input of the subsequent discriminator. The discriminator of GAN-MDF identifies whether the generator output is a real sample generated from HF model. To enhance the stability of GAN-MDF's training, we also introduce the supervised-loss trick to refine the generator weights during each iteration of the adversarial training. Compared with the state-of-the-art methods, the proposed GAN-MDF has the following advantages: 1) it performs well in the case of either nested or unnested sample structure; 2) there is no specific assumption on the data distribution; and 3) it has high robustness even when very few HF samples are provided. The experimental results also support the validity of GAN-MDF.

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