In this presentation, we will take a journey into the transformative potential of artificial intelligence (AI) in generating synthetic data to bolster the effectiveness and efficiency of digital twin and digital thread use cases. As the demand for high-quality data surges, AI's ability to synthesize data addresses challenges like data scarcity, privacy concerns, and real-world biases. Through various AI techniques, such as Generative Adversarial Networks and Variational Autoencoders, industries can accelerate the deployment and optimization of digital twins and threads. While the promise is vast, we also must consider the ethical dimensions, ensuring the synthetic data does not perpetuate biases or breach privacy norms.
