Existing battery State of Health (SOH) prediction approaches often struggle to provide both accurate predictions and reliable uncertainty estimates. This paper presents a novel Multi-Task Learning (MTL) framework that jointly tackles SOH prediction and provides a proxy metric for uncertainty through a unified architecture. The framework combines a Physics-Informed Neural Network (PINN) for SOH prediction with a deep autoencoding Gaussian mixture model for uncertainty modeling. Particularly, the energy score from the Gaussian mixture model serves as a proxy metric for uncertainty, where a higher score indicates potential prediction unreliability. Moreover, to enhance task-specific learning, we employ a multi-head attention mechanism that adaptively captures distinct feature relationships. Our experiments show improvements in prediction performance compared to the state-of-the-art baseline. A comprehensive evaluation on six XJTU battery benchmark datasets demonstrates that our framework achieves a prediction accuracy of 99.50% (MAPE: 0.0050) while providing reliable uncertainty quantification through the proxy metric.

A modern data center is supported by an Internet of Assets (IoA), a specialized Internet of Things (IoT), where the sim-ready assets encompass physical properties and connected data streams for passive data collection and proactive simulation. A digital twin (DT) is a virtual replica of the IoA system with a proper level of abstraction, integrating asset-level dynamics models to simulate system-wide behavior, prototype designs, and conduct what-if analysis. However, creating high-fidelity DTs is hindered by the manual effort needed to encode complex geometric layouts and domain-specific design constraints. While large language models (LLMs) offer automation potential, existing methods struggle to generate geometrically plausible and functionally valid DT scenes due to limited domain integration. To bridge the gaps, we propose ChatDC, a conversational system that leverages LLMs to automate data center digital twin generation through a S egment- G enerate- O ptimize (SGO) workflow. ChatDC integrates domain knowledge via a dedicated code library named DCBuild and employs SGO to decompose user prompts, generate initial structures, and optimize layouts in compliance with data center design constraints. Evaluation shows that ChatDC outperforms other baselines with 98% success rate on scratch generation tasks and reduces the average makespan by 10×. Ablation study reveals that the SGO design increases the generation success rate by 65% at most. Furthermore, computational fluid dynamics simulations validate the physical plausibility, confirming the readiness of generated DTs for real-world analysis.

This paper considers the carbon-aware data center (DC) capacity provisioning problem under uncertain green energy availability and computing demand. To address it, accurate carbon emissions estimation and robust capacity provisioning are necessary. Existing studies mainly consider the carbon footprint of the computing system and merely consider that of the physical facilities, which also contribute significant carbon emissions. Furthermore, their capacity provisioning is neither uncertainty-aware nor adaptive to the dynamic computing demand. To bridge these gaps, we propose an adaptive capacity provisioning framework based on the physics-informed digital twin. We design the digital twin to holistically capture a DC's operational carbon footprint, including both the computing system and the physical facilities. The digital twin is differentiable and established with a collection of physics-informed learnable models that are learned with online operational data. We further address the challenge of capacity provisioning under uncertainties by designing a shrinking horizon model predictive control. The designed capacity planner updates its estimation of future computing demand based on the observable computing system states. At each capacity provisioning round, we solve the capacity provisioning problem using a gradient-based optimization technique with the gradient provided by the digital twin. We extensively evaluate our approach usingrealoperational data from a large-scale production data center. First, our digital twin accurately predicts holistic DC energy usage with a relative absolute error of less than 5%, which is accurate according to the industrial rule of thumb. Second, we show that our solution is comparable to the oracle solution with perfect knowledge about all uncertainties, outperforming the state-of-the-art Predict-then-Plan approach significantly in terms of SLO violation reduction. Furthermore, our approach reduces carbon footprint by 27% compared with the over-provisioning scheme currently adopted by the industry.

Few-shot object detection aims at detecting previously unseen objects given only a few annotated samples. Most existing approaches treat the model obtained from the base training stage with abundant data as a container of prior knowledge that can be transferred to novel objects. Knowledge with similar properties is also contained in Contrastive Language-Image Pretraining (CLIP). In this paper, we utilize this external prior knowledge to generate proposal-level classification scores to improve the detection results. We notice that these scores can hardly reflect the quality of proposal localization, so we combine them with the ones from a conventional detector to obtain the ability to distinguish the background. Moreover, we propose a new score fusion module with regularization to alleviate the ambiguity of detection results generated by a trivial element-wise multiplication fusion method. To further improve the quality of classification scores in our proposed branch, we add learnable prompts to mitigate the inaccurate classification problem we observe. We conduct extensive experiments on the PASCAL VOC dataset and demonstrate the effectiveness of our approach.

Deep reinforcement learning (DRL) has shown good performance in tackling Markov decision process (MDP) problems. As DRL optimizes a long-term reward, it is a promising approach to improving the energy efficiency of data-center cooling. However, enforcement of thermal safety constraints during DRL’s state exploration is a main challenge. The widely adopted reward-shaping approach adds negative reward when the exploratory action results in unsafety. Thus, it needs to experience sufficient unsafe states before it learns how to prevent unsafety. In this article, we propose a safety-aware DRL framework for data-center cooling control. It applies offline imitation learning and online post-hoc rectification to holistically prevent thermal unsafety during online DRL. In particular, the post-hoc rectification searches for the minimum modification to the DRL-recommended action such that the rectified action will not result in unsafety. The rectification is designed based on a thermal state transition model that is fitted using historical safe operation traces and able to extrapolate the transitions to unsafe states explored by DRL. Extensive evaluation for chilled water and direct expansion-cooled data centers in two climate conditions show that our approach saves 18% to 26.6% of total data-center power compared with conventional control and reduces safety violations by 94.5% to 99% compared with reward shaping. We also extend the proposed framework to address data centers with non-uniform temperature distributions for detailed safety considerations. The evaluation shows that our approach saves 14% power usage compared with the PID control while addressing safety compliance during the training.