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All issues Volume 716 (2026) E3S Web Conf., 716 (2026) 02018 References
Open Access
Issue
E3S Web Conf.
Volume 716, 2026
The 12th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC 2026)
Article Number 02018
Number of page(s) 8
Section Building Technology and Performance
DOI https://doi.org/10.1051/e3sconf/202671602018
Published online 09 June 2026
  1. Saridou, B., et al. Thermal management in large data centres: security threats and mitigation. in Eighth International Symposium on Security in Computing and Communications. 2021. Springer. [Google Scholar]
  2. Cohen, A., et al. Online linear quadratic control. in International Conference on Machine Learning. 2018. PMLR. [Google Scholar]
  3. Liu, G., et al., Reconstruction and fast prediction of 3D heat and mass transfer based on a variational autoencoder. International Communications in Heat and Mass Transfer, 2024. 351: p. 119764. [Google Scholar]
  4. Wang, R., et al., Toward Physics-Informed Machine Learning for Data Center Operations: A Tropical Case Study. arXiv preprint arXiv:2505.19414, 2025. [Google Scholar]
  5. Soleymani, M., M. Bonyani, and C. Wang, Lightweight detection of small tools for safer construction. Automation in Construction, 2024. 351: p. 119764. [Google Scholar]
  6. Fayolle, P., et al. Physics-Informed Autoencoder for DSC-MRI Perfusion Post-Processing: Application to Glioma Grading. in 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). 2025. IEEE. [Google Scholar]
  7. Thelen, A., et al., A comprehensive review of digital twin--part 1: modeling and twinning enabling technologies. Structural and Multidisciplinary Optimization, 2022. 65(12): p. 354. [Google Scholar]
  8. Bonyani, M., M. Soleymani, and C. Wang, Occupancy-basedHVAC Control through Process-driven Models with Data-driven Approach Using Deep Reinforcement Learning. [Google Scholar]
  9. Chen, X., et al., Physics-Informed Predictive Causality in Data Center Cooling. Electronics, 2025. 14(21): p. 4231. [Google Scholar]
  10. Naidu, S.M. and N. Xiong. S2DEVFMAP: SelfSupervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries. in Proceedings of the 2024 9th International Conference on Machine Learning Technologies. 2024. [Google Scholar]
  11. Holly, S., et al. Autoencoder Based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems. in PHM Society European Conference. 2022. [Google Scholar]
  12. Soleymani, M., M. Bonyani, and C. Wang, Automatic Construction Exoskeleton Gait Data Generation Using Variational Autoencoders and Optimized Features for Human-Centric Exoskeleton Power Control, in Computing in Civil Engineering 2024. p. 841-850. [Google Scholar]
  13. Gao, Y., et al., Physics-embedded Recurrent Graph Neural Network for Fault Diagnosis of Complex Systems. IEEE Access, 2024. [Google Scholar]
  14. Berezovskaya, Y., et al. A hybrid fault detection and diagnosis method in server rooms, cooling systems. in 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). 2019. IEEE. [Google Scholar]
  15. Dave, A.J. and R.B. Vilim, Physics-informed Statespace Neural Networks for transport phenomena. Engineering Applications of Artificial Intelligence, 2024. 351: p. 119764. [Google Scholar]
  16. Jiang, Z., et al., Physics-informed machine learning for building performance simulation-A review of a nascent field. Advances in Applied Energy, 2025: p. 100223. [Google Scholar]
  17. Gao, T., et al. Data mining and data-driven modelling for Air Handling Unit fault detection. in E3s Web of Conferences. 2019. EDP Sciences. [Google Scholar]
  18. Soleimani, M., F. Campean, and D. Neagu, Diagnostics and prognostics for complex systems: A review of methods and challenges. Quality and Reliability Engineering International, 2021. 37(8): p. 3746-3778. [Google Scholar]
  19. Kaushik, K. and V. Naik, Detecting faults in the cooling systems by monitoring temperature and energy. Energy Informatics, 2024. 7(1): p. 46. [Google Scholar]
  20. Zhan, X., et al. Data Center Cooling System Optimization Using Offline Reinforcement Learning. in The Thirteenth International Conference on Learning Representations. [Google Scholar]
  21. Soleymani, M., M. Bonyani, and C. Wang, Skeletonbased action recognition for manufacturing assembly task through graph convolution network. Journal of Manufacturing Systems, 2024. 351: p. 119764. [Google Scholar]
  22. Brännvall, R., J. Gustafsson, and F. Sandin, Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers. Energies, 2023. 16(5): p. 2255. [Google Scholar]
  23. Chen, J., et al., Knowledge embedded autoencoder network for harmonic drive fault diagnosis under few-shot industrial scenarios. IEEE Internet of things journal, 2024. 11(13): p. 22915-22925. [Google Scholar]
  24. Fukagata, K. and K. Fukami, Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control. Fluid Dynamics Research, 2025. [Google Scholar]
  25. Bonyani, M., M. Soleymani, and C. Wang, Useragnostic adaptation of human locomotion intent: Leveraging Teacher-Student-Learning and ensemble modeling. Knowledge-Based Systems, 2024. 351: p. 119764. [Google Scholar]
  26. Feng, Y., et al. Auto-Linear Phenomenon in Subsurface Imaging. in International Conference on Machine Learning. 2024. PMLR. [Google Scholar]
  27. He, K., et al. Masked autoencoders are scalable vision learners. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022. [Google Scholar]
  28. Wu, S., et al., AlphaDataCenterCooling: A virtual testbed for evaluating operational strategies in data center cooling plants. Applied Energy, 2024. 351: p. 119764. [Google Scholar]
  29. Nambiar, C., M. Rosenberg, and S. Rosenberg, End Use Analysis Of ANSI/ASHRAE/IES Standard 90.12019. ASHRAE Journal, 2023. 65(4): p. 34-42. [Google Scholar]
  30. Malhotra, P., LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection. arXiv preprint arXiv:1607.00148, 2016. [Google Scholar]
  31. Zong, B., et al. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. in International conference on learning representations. 2018. [Google Scholar]
  32. Velicković, P., et al., Graph attention networks. arXiv preprint arXiv:1710.10903, 2017. [Google Scholar]
  33. Zia, S. and N. Bibi. Enhanced anomaly detection in IoT: A transformer based approach for multivariate time series data. in 2024 International Conference on Engineering & Computing Technologies (ICECT). 2024. IEEE. [Google Scholar]
  34. Liao, J., et al., DPDGAD: A Dual-Process Dynamic Graph-based Anomaly Detection for multivariate time series analysis in cyber-physical systems. Advanced Engineering Informatics, 2024. 351: p. 119764. [Google Scholar]
  35. Pan, C., et al., CT-DDPM: Anomaly detection of multivariate time series with copula and transformer-based denoising diffusion probabilistic models. Information Sciences, 2024. 351: p. 119764. [Google Scholar]

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