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

arXiv: https://arxiv.org/abs/2106.14655 · HTML: https://mlsanity.com/paper/2106.14655/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Survey of multifidelity methods in uncertainty propagation, inference, and optimization](https://mlsanity.com/paper/1806.10761/) (2018)
- [Deep Gaussian Processes for Multi-fidelity Modeling](https://mlsanity.com/paper/1903.07320/) (2019)
- [A support vector regression-based multi-fidelity surrogate model](https://mlsanity.com/paper/1906.09439/) (2019)

## Built on by (most cited first, 1 of 1)

- [Revisiting PINNs: Generative Adversarial Physics-informed Neural Networks and Point-weighting Method](https://mlsanity.com/paper/2205.08754/) (2022)

## Abstract (arXiv)

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.