  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.
