Fast 3D Surrogate Modeling for Data Center Thermal Management

• URL: http://arxiv.org/abs/2511.11722v1
• Date: Thu, 13 Nov 2025 02:12:24 GMT
• Title: Fast 3D Surrogate Modeling for Data Center Thermal Management
• Authors: Soumyendu Sarkar, Antonio Guillen-Perez, Zachariah J Carmichael, Avisek Naug, Refik Mert Cam, Vineet Gundecha, Ashwin Ramesh Babu, Sahand Ghorbanpour, Ricardo Luna Gutierrez,
• Abstract summary: Traditional thermal CFD solvers are computationally expensive and require expert-crafted meshes and boundary conditions.<n>We develop a vision-based surrogate modeling framework that operates directly on a 3D voxelized representation of the data center.<n>Our results show that the surrogate models generalize across data center configurations and achieve up to 20,000x speedup.
• Score: 15.644716872105002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reducing energy consumption and carbon emissions in data centers by enabling real-time temperature prediction is critical for sustainability and operational efficiency. Achieving this requires accurate modeling of the 3D temperature field to capture airflow dynamics and thermal interactions under varying operating conditions. Traditional thermal CFD solvers, while accurate, are computationally expensive and require expert-crafted meshes and boundary conditions, making them impractical for real-time use. To address these limitations, we develop a vision-based surrogate modeling framework that operates directly on a 3D voxelized representation of the data center, incorporating server workloads, fan speeds, and HVAC temperature set points. We evaluate multiple architectures, including 3D CNN U-Net variants, a 3D Fourier Neural Operator, and 3D vision transformers, to map these thermal inputs to high-fidelity heat maps. Our results show that the surrogate models generalize across data center configurations and achieve up to 20,000x speedup (hundreds of milliseconds vs. hours). This fast and accurate estimation of hot spots and temperature distribution enables real-time cooling control and workload redistribution, leading to substantial energy savings (7\%) and reduced carbon footprint.

Related papers

• Multimodal Signal Processing For Thermo-Visible-Lidar Fusion In Real-time 3D Semantic Mapping [8.401699100150866]
  This paper presents a novel method for semantically enhancing 3D point cloud maps with thermal information.<n>The system projects real-time LiDAR point clouds onto this fused image stream.<n>It then segments heat source features in the thermal channel to instantly identify high temperature targets and applies this temperature information as a semantic layer on the final 3D map.
  arXiv  Detail & Related papers  (2026-01-14T15:46:57Z)
• ThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based Refinement [9.923805440410739]
  We introduce ThermoStereoRT, a real-time thermal stereo matching method.<n>It recovers disparity from two rectified thermal stereo images.<n>We envision applications such as night-time drone surveillance or under-bed cleaning robots.
  arXiv  Detail & Related papers  (2025-04-10T03:24:21Z)
• Data-efficient rapid prediction of urban airflow and temperature fields for complex building geometries [0.0]
  We propose a novel framework that leverages a multi-directional distance feature (MDDF) combined with localized training to achieve effective wind field predictions.<n>We trained on only 24 CFD simulations, our localized Fourier neural operator (Local-FNO) model generates full 3D wind velocity and temperature predictions in under one minute.<n>With mean absolute errors of 0.3 m/s for wind speed and 0.3 $circ$C for temperature on unseen urban configurations, our method demonstrates strong generalization capabilities and significant potential for practical urban applications.
  arXiv  Detail & Related papers  (2025-03-25T14:36:01Z)
• Transforming Weather Data from Pixel to Latent Space [57.80389860291812]
  We propose a novel Weather Latent Autoencoder that transforms weather data from pixel space to latent space.<n>We demonstrate its superior compression and reconstruction performance, enabling the creation of the ERA5-latent dataset.
  arXiv  Detail & Related papers  (2025-03-09T13:55:33Z)
• Neural Network Surrogate Model for Junction Temperature and Hotspot Position in $3$D Multi-Layer High Bandwidth Memory (HBM) Chiplets under Varying Thermal Conditions [6.277496992820668]
  This work develops a data-driven neural network model for the fast prediction of junction temperature and hotspot position in 3D chiplets.<n>It shows good generalizability for other thermal conditions not considered in the parameter space.<n>It makes it a valuable tool for improving thermal management and performance in high-performance computing applications.
  arXiv  Detail & Related papers  (2025-03-06T03:05:54Z)
• RS-vHeat: Heat Conduction Guided Efficient Remote Sensing Foundation Model [59.37279559684668]
  We introduce RS-vHeat, an efficient multi-modal remote sensing foundation model.<n>Specifically, RS-vHeat applies the Heat Conduction Operator (HCO) with a complexity of $O(N1.5)$ and a global receptive field.<n>Compared to attention-based remote sensing foundation models, we reduce memory usage by 84%, FLOPs by 24% and improves throughput by 2.7 times.
  arXiv  Detail & Related papers  (2024-11-27T01:43:38Z)
• Binocular Model: A deep learning solution for online melt pool temperature analysis using dual-wavelength Imaging Pyrometry [0.0]
  In metal Additive Manufacturing (AM), monitoring the temperature of the Melt Pool (MP) is crucial for ensuring part quality, process stability, defect prevention, and overall process optimization. Traditional methods, are slow to converge and require extensive manual effort to translate data into actionable insights. We propose an Artificial Intelligence (AI)-based solution aimed at reducing manual data processing reliance.
  arXiv  Detail & Related papers  (2024-08-20T18:26:09Z)
• Atmospheric Transport Modeling of CO$_2$ with Neural Networks [46.26819563674888]
  Accurately describing the distribution of CO$$ in the atmosphere with atmospheric tracer transport models is essential for greenhouse gas monitoring and verification support systems. Large deep neural networks are poised to revolutionize weather prediction, which requires 3D modeling of the atmosphere. In this study we explore four different deep neural networks which have proven as state-of-the-art in weather prediction to assess their usefulness for atmospheric tracer transport modeling.
  arXiv  Detail & Related papers  (2024-08-20T17:33:20Z)
• EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes [55.2480439325792]
  We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation.<n>In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments.
  arXiv  Detail & Related papers  (2024-05-27T17:19:02Z)
• Building Vision Models upon Heat Conduction [66.1594989193046]
  This study introduces the Heat Conduction Operator (HCO) built upon the physical heat conduction principle.<n>HCO conceptualizes image patches as heat sources and models their correlations through adaptive thermal energy diffusion.<n> vHeat achieves up to a 3x throughput, 80% less GPU memory allocation, and 35% fewer computational FLOPs compared to the Swin-Transformer.
  arXiv  Detail & Related papers  (2024-05-26T12:58:04Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.