## 1 Introduction  
Data centers constitute the critical information infrastructure underpinning global digitalization, with accelerating service demand driven by data-intensive technologies, including artificial intelligence, smart energy systems, distributed manufacturing, and autonomous vehicles [1]. This growth trajectory, however, engenders substantial sustainability challenges: data centers contribute to roughly 1% of global energy consumption and 0.3% of worldwide carbon dioxide emissions [1,2], and this energy consumption has doubled every four years over the past decade [3]. Such resource intensity directly contravenes international carbon neutrality imperatives, positioning energy optimization in data centers as a research priority of significant environmental and economic consequence. Energy consumption in data centers mainly attributes to the cooling systems, which occupy nearly 50% of the total energy usage [4,5]. Power Usage Effectiveness (PUE), defined as the ratio of total facility energy to \(\Pi\) equipment energy, has emerged as the principal metric for evaluating energy efficiency. The lower the PUE, the higher the energy efficiency of a data center. Reducing PUE from present averages of nearly 1.4 towards optimal theoretical values of 1.0 requires significant improvements in cooling strategies, especially as server densities continue to increase. High-efficiency cooling strategies have drawn significant attention to lower PUE. Beyond traditional data centers, the concept of intelligent data centers (IDCs) has been recently proposed with the development of artificial intelligence (AI) technology. For example, the DeepMind group from Google [6; 7] has implemented AI-controlled water-cooling systems to save energy consumption of Google cloud data centers. The performance leveraging AI techniques has shown a decline in annual average PUE to ~1.1, which saves a total energy consumption of 15-40%. Aiming to achieve the intelligent thermal management, real-time temperature prediction of data center and smart cooling decision have shown to play significant roles in IDC [8]. Previous research of real-time temperature prediction for IDC has mainly focused on the development of a data-driven method, which realizes a prediction error of about 3% at the inlet temperature of the server rack [9]. Recently, physics-informed neural networks (PINNs) [10; 11; 12] and physics-guided machine learning (PGML) [13] are further developed for IDC flow and heat transfer simulations. Leveraging the advantages of computational fluid dynamics (CFD) [14], the machine-learning technique provides an alternative fast and accurate method to accelerate CFD [15; 16; 17; 18] for IDC applications. In order to comprehensively assess thermal performance, the requirement of high-fidelity temperature field reconstruction is still significant during the decision-making process. Furthermore, optimizing cooling decision-making in IDC is actually a trade-off between the performance and energy consumption, where AI has made a great contribution [19; 20; 21; 22; 23]. Deep Reinforcement Learning (DRL) are developed to make cooling policy to optimize the tradeoff between energy-consuming constraints and PUE reduction targets [24; 25; 26; 27; 28]. Deep Q-Network Agent (DQN), as one of the DRL algorithms [29], is considered a powerful tool to make smart cooling decisions in this work due to its great potential in unknown environment decision-making and performance enhancement [30; 31; 32]. However, DRL algorithms frequently prioritize singular objectives (e.g., PUE minimization) while neglecting thermal homogeneity constraints due to too few detecting temperature sensors. In addition, implementation frameworks lack integrated visualization and control interfaces for operational deployment. A digital twin platform [23; 33; 34; 35] further fulfils the integrated functions, which can be potentially used to equip with the real-time thermal prediction model and the intelligent decision-making method for IDC systems.