Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development

TL;DR

The paper tackles the problem of constructing digital twins that faithfully replicate physical-space dynamics to enable safe, low-cost testing of algorithmic models under distribution shift. It introduces an intelligent construction framework based on policy-gradient optimization with a feature-preprocessing module (PGFP) that rapidly derives state-transition parameters from physical-space sequences. A novel evaluation metric, Mean State Error ($\text{MSTE}$), quantifies how well digital-space trajectories align with physical-space trajectories. Empirical validation in a tower-defense simulation shows that PGFP outperforms genetic-algorithm and standard policy-gradient baselines, supporting its potential for applications in wireless networks and industrial production. Formally, the problem is framed as minimizing $\min_{\boldsymbol{\theta}} \sum_{i=1}^{N} \|\hat{\boldsymbol{s}}_i - \boldsymbol{s}_i\|$ subject to system dynamics, with $\text{MSTE} = \sum_{i=1}^{N} \|\hat{\boldsymbol{s}}_i - \boldsymbol{s}_i\|$ and policy-gradient updates given by $\nabla_{\boldsymbol{\varphi}}\bar{\vartheta}=\mathbb{E}_{\boldsymbol{\theta}\sim\pi}[\vartheta_{\boldsymbol{\theta}}\nabla_{\boldsymbol{\varphi}}\log\pi(\Delta\hat{\boldsymbol{s}};\boldsymbol{\varphi})]$. These components collectively enable rapid, faithful DT construction with practical impact on real-world testing and deployment.

Abstract

The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict the dynamics of real-world systems remains substantial. This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance. We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems. Moreover, the Mean STate Error (MSTE) is proposed as a robust metric for evaluating the performance of algorithms within these digital space. The efficacy of our framework is demonstrated through extensive simulations that show our DT not only accurately mirrors the physical reality but also provides a reliable platform for algorithm evaluation. This work lays a foundation for future research into DT technologies, highlighting pathways for both theoretical enhancements and practical implementations in various industries.

Figures (4)

• Figure 1: Illustration of proposed method.
• Figure 2: Convergence performance of different methods.
• Figure 3: Prediction of parameter 1 by different methods.
• Figure 4: Prediction of parameter 2 by different methods.