GAN-MDF: An Enabling Method for Multifidelity Data Fusion

Abstract

NVIDIA Omniverse offers a unified platform for 3-D production pipelines based on the digital twins of real physical systems. The Internet of Things facilitates the acquisition of Omniverse data from different information sources, including finite-element model simulations and various sensors, and such data describe the responses of physical systems. According to their response description accuracies, these multisource data can be divided into different fidelity levels. High-fidelity (HF) data describe responses of the given system accurately but are costly to obtain. In contrast, low-fidelity (LF) data are inexpensive but often do not reach the desired accuracy level. Multifidelity data fusion (MDF) aims to use massive LF data and small amounts of HF data to develop the digital twin of a real physical system to produce accurate digital system responses. In this article, we propose a novel generative adversarial network for MDF. Experimental results show that the proposed model performs better than the state-of-the-art methods without any specific assumptions regarding the data distribution or data structure and has higher stability when addressing varying amounts of HF and LF data, especially in cases with very few HF data.