At IMOS, our research encompasses both methodological contributions and practical applications in Prognostics and Health Management. Our methodological advancements span across diverse fields such as PHM, computer vision, and signal processing, while our applications address various domains, including energy and transportation. Through our work, we strive to bridge the gap between theoretical developments and real-world challenges.
Methodological Contributions
List of all Publications
Algorithm-informed graph neural networks for leakage detection and localization in water distribution networks
Z. Zhang; O. Fink
Reliability Engineering and System Safety. 2026. Vol. 265, p. 111494. DOI : 10.1016/j.ress.2025.111494. Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring
W. Hu; G. Frusque; T. Wang; F. Chu; O. Fink
Reliability Engineering and System Safety. 2025. Vol. 264, p. 111397. DOI : 10.1016/j.ress.2025.111397. Equi-Euler GraphNet: An equivariant, temporal-dynamics informed graph neural network for dual force and trajectory prediction in multi-body systems
V. Sharma; R. T. Oddon; P. Tesini; J. Ravesloot; C. Taal et al.
Mechanical Systems and Signal Processing. 2025. Vol. 241, p. 113533. DOI : 10.1016/j.ymssp.2025.113533. Interpretable prognostics with concept bottleneck models
F. Forest; K. Rombach; O. Fink
Information Fusion. 2025. Vol. 124, p. 103427. DOI : 10.1016/j.inffus.2025.103427. Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers
C. C. Hsu; G. Frusque; F. Forest; F. Macedo; C. M. Franck et al.
Reliability Engineering and System Safety. 2025. Vol. 263, p. 111199. DOI : 10.1016/j.ress.2025.111199. Weighted Sum-Rate Maximization for Beamforming Design Using Minorization-Maximization: Convergence Rate and Deep Unfolding
Z. Yang; Z. Zhang; Z. Zhao
2025. 33rd European Signal Processing Conference, Palermo, Italy, 2025-09-08 – 2025-09-12. p. 2457 – 2461. DOI : 10.23919/eusipco63237.2025.11226730. Combining physics-based and data-driven modeling for building energy systems
L. Von Krannichfeldt; K. Orehounig; O. Fink
Applied Energy. 2025. Vol. 391, p. 125853. DOI : 10.1016/j.apenergy.2025.125853. Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics
A. Wei; O. Fink
NATURE COMMUNICATIONS. 2025. Vol. 16, num. 1. DOI : 10.1038/s41467-025-62250-7. Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning
M. Montazeri; C. S. Kulkarni; O. Fink
Reliability Engineering & System Safety. 2025. Vol. 259, p. 1 – 14. DOI : 10.1016/j.ress.2025.110897. GRADE: Generating Realistic and Dynamic Environments for robotics research with Isaac Sim
E. Bonetto; C. Xu; A. Ahmad
The International Journal of Robotics Research. 2025. DOI : 10.1177/02783649251346211. Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures
G. Floreale; P. Baraldi; E. Zio; O. Fink
Engineering Applications of Artificial Intelligence. 2025. Vol. 149, p. 110518. DOI : 10.1016/j.engappai.2025.110518. ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades
M. Hassan; F. Forest; O. Fink; M. Mielle
Advanced Engineering Informatics. 2025. Vol. 65, p. 103345. DOI : 10.1016/j.aei.2025.103345. Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics
M. Zhao; C. Taal; S. Baggerohr; O. Fink
Mechanical Systems and Signal Processing. 2025. Vol. 230, p. 112544. DOI : 10.1016/j.ymssp.2025.112544. Interactive symbolic regression with co-design mechanism through offline reinforcement learning
Y. Tian; W. Zhou; M. Viscione; H. Dong; D. S. Kammer et al.
Nature Communications. 2025. Vol. 16. DOI : 10.1038/s41467-025-59288-y. Domain Adaptive Unfolded Graph Neural Networks
Z. Zhang; O. Fink
2025. The 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, Pennsylvania, USA, 2025-02-25 – 2025-03-04. p. 22714 – 22722. DOI : 10.1609/aaai.v39i21.34431. Power for AI and AI for Power: The Infinite Entanglement Between Artificial Intelligence and Power Electronics Systems
M. Chen; H. Cui; F. Blaabjerg; L. Lorenz; R. Hellinger et al.
IEEE POWER ELECTRONICS MAGAZINE. 2025. Vol. 12, num. 1, p. 37 – 43. DOI : 10.1109/MPEL.2024.3524742. Exploiting semantic scene reconstruction for estimating building envelope characteristics
C. Xu; M. Mielle; A. Laborde; A. Waseem; F. Forest et al.
Building and Environment. 2025. DOI : 10.1016/j.buildenv.2025.112731. Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion
K. F. Niresi; I. Nejjar; O. Fink
IEEE Internet of Things Journal. 2025. p. 1 – 1. DOI : 10.1109/JIOT.2025.3597150. Graph Neural Networks With Adaptive Structures
Z. Zhang; S. Lu; Z. Huang; Z. Zhao
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING. 2025. Vol. 19, num. 1, p. 181 – 194. DOI : 10.1109/JSTSP.2024.3485892.
