Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology

• URL: http://arxiv.org/abs/2408.12634v1
• Date: Thu, 22 Aug 2024 14:08:45 GMT
• Title: Joint Hypergraph Rewiring and Memory-Augmented Forecasting Techniques in Digital Twin Technology
• Authors: Sagar Srinivas Sakhinana, Krishna Sai Sudhir Aripirala, Shivam Gupta, Venkataramana Runkana,
• Abstract summary: Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios. We introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge.
• Score: 2.368662284133926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Digital Twin technology creates virtual replicas of physical objects, processes, or systems by replicating their properties, data, and behaviors. This advanced technology offers a range of intelligent functionalities, such as modeling, simulation, and data-driven decision-making, that facilitate design optimization, performance estimation, and monitoring operations. Forecasting plays a pivotal role in Digital Twin technology, as it enables the prediction of future outcomes, supports informed decision-making, minimizes risks, driving improvements in efficiency, productivity, and cost reduction. Recently, Digital Twin technology has leveraged Graph forecasting techniques in large-scale complex sensor networks to enable accurate forecasting and simulation of diverse scenarios, fostering proactive and data-driven decision making. However, existing Graph forecasting techniques lack scalability for many real-world applications. They have limited ability to adapt to non-stationary environments, retain past knowledge, lack a mechanism to capture the higher order spatio-temporal dynamics, and estimate uncertainty in model predictions. To surmount the challenges, we introduce a hybrid architecture that enhances the hypergraph representation learning backbone by incorporating fast adaptation to new patterns and memory-based retrieval of past knowledge. This balance aims to improve the slowly-learned backbone and achieve better performance in adapting to recent changes. In addition, it models the time-varying uncertainty of multi-horizon forecasts, providing estimates of prediction uncertainty. Our forecasting architecture has been validated through ablation studies and has demonstrated promising results across multiple benchmark datasets, surpassing state-ofthe-art forecasting methods by a significant margin.

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