Publications ‒ IMOS ‐ EPFL

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

Practical Applications

List of all Publications

From physics to machine learning and back: Part I – Learning with inductive biases in prognostics and health management (PHM)

O. Fink; V. Sharma; I. Nejjar; L. Von Krannichfeldt; S. Garmaev et al.

Reliability Engineering and System Safety. 2026. Vol. 271, p. 112213. DOI : 10.1016/j.ress.2026.112213.

Methodological Contributions

Practical Applications

List of all Publications

From physics to machine learning and back: Part I – Learning with inductive biases in prognostics and health management (PHM)

Time-Vertex machine learning for optimal sensor placement in temporal graph signals: Applications in structural health monitoring

Uncertainty-guided alignment for unsupervised domain adaptation in regression

Loc2: Interpretable Cross-View Localization via Depth-Lifted Local Feature Matching

Heterogeneous Graph Neural Networks for Short-term State Forecasting in Power Systems across Domains and Time Scales: A Hydroelectric Power Plant Case Study

Integrating Physics-based and Data-driven Approaches for Probabilistic Building Energy Modeling

From Physics to Machine Learning and Back: Part II – Learning and Observational Bias in Prognostics and Health Management (PHM)

A physics-informed graph neural network conserving linear and angular momentum for dynamical systems

TARD: Test-time Domain Adaptation for Robust Fault Detection under Evolving Operating Conditions

Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models

Algorithm-informed graph neural networks for leakage detection and localization in water distribution networks

Interpretable prognostics with concept bottleneck models

Equi-Euler GraphNet: An equivariant, temporal-dynamics informed graph neural network for dual force and trajectory prediction in multi-body systems

Classifier-free diffusion-based weakly-supervised approach for health indicator derivation in rotating machines: Advancing early fault detection and condition monitoring

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

Thermoxels: a voxel-based method to generate simulation-ready 3D thermal models

Weighted Sum-Rate Maximization for Beamforming Design Using Minorization-Maximization: Convergence Rate and Deep Unfolding

Combining physics-based and data-driven modeling for building energy systems

Learning physics-consistent material behavior from dynamic displacements

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics