Publications de Olga Fink | EPFL Graph Search

Interpretable prognostics with concept bottleneck models

Deep learning approaches have recently been extensively explored for the prognostics of industrial assets. However, they still suffer from a lack of interpretability, which hinders their adoption in safety-critical applications. To improve their trustworth ...

2025

Prescribing optimal health-aware operation for urban air mobility with deep reinforcement learning

Olga Fink

Urban Air Mobility (UAM) aims to expand existing transportation networks in metropolitan areas by offering short flights either to transport passengers or cargo. Electric vertical takeoff and landing aircraft powered by lithium-ion battery packs are consid ...

Elsevier BV2025

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

Olga Fink

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

NATURE PORTFOLIO2025

ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades

Mariam Ahmed Mahmoud Hegazy Hassan, Florent Evariste Forest, Olga Fink

Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, i ...

2025

Power for AI and AI for Power: The Infinite Entanglement Between Artificial Intelligence and Power Electronics Systems

Olga Fink

This article summarizes the discussions and outcomes of the "Power for AI and AI for Power" session at the IEEE Power Electronics Society (PELS) Future of Electronic Power Processing and Conversion (FEPPCON XII). Power electronics embeds intelligence into ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

K-space physics-informed neural network (k-PINN) for compressed spectral mapping and efficient inversion of vibrations in thin composite laminates

Olga Fink

The vibrational response of structural components carries valuable information about their underlying mechanical properties, health status and operational conditions. This underscores the need for the development of efficient physics-based inversion algori ...

Elsevier BV2025

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

Olga Fink, Gaëtan Michel Frusque

Deriving health indicators of rotating machines is crucial for their maintenance. However, this process is challenging for the prevalent adopted intelligent methods since they may take the whole data distributions, not only introducing noise interference b ...

2025

Graph neural networks for virtual sensing in complex systems: Addressing heterogeneous temporal dynamics

Olga Fink, Mengjie Zhao

Real-time condition monitoring is crucial for the reliable and efficient operation of complex systems. However, relying solely on physical sensors can be limited due to their cost, placement constraints, or inability to directly measure certain critical pa ...

2025

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

Olga Fink

Building energy modeling plays a vital role in optimizing the operation of building energy systems by providing accurate predictions of the building's real-world conditions. In this context, various techniques have been explored, ranging from traditional p ...

2025

Interactive symbolic regression with co-design mechanism through offline reinforcement learning

Olga Fink, Michele Viscione

Symbolic Regression holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possible expressions poses significant challenges for previous online search methods an ...

Springer Science and Business Media LLC2025

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

Florent Evariste Forest, Olga Fink, Gaëtan Michel Frusque

Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfun ...

2025

Continuous Health State Monitoring of High Voltage Circuit Breakers

Olga Fink, Gaëtan Michel Frusque

Circuit breakers (CBs) are renowned for their high reliability and long lifespan. As a result, many CBs installed decades ago are now approaching their predefined end of service life. However, this predefined service life does not always reflect the actual ...

Institute of Electrical and Electronics Engineers (IEEE)2025

Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion

Ismail Nejjar, Olga Fink

The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these systems often face challenges related to degraded data quality caused by sensor dr ...

2025

Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-training Strategies and Performance Insights

Florent Evariste Forest, Olga Fink

This paper focuses on source-free domain adaptation for object detection in computer vision. This task is challenging and of great practical interest, due to the cost of obtaining annotated data sets for every new domain. Recent research has proposed vario ...

Springer Science and Business Media Deutschland GmbH2025

Exploiting semantic scene reconstruction for estimating building envelope characteristics

Florent Evariste Forest, Olga Fink

Precise assessment of geometric building envelope characteristics is essential for parametric simulation analysis and informed retrofitting decisions. Previous methods for estimating building characteristics, such as window-to-wall ratio and building footp ...

Elsevier BV2025

Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures

Olga Fink

Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address these issues but manually analyzing expl ...

2025

Anomaly Anything: Promptable Unseen Visual Anomaly Generation

Olga Fink, Han Sun

Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive tra ...

2025

Domain Adaptive Unfolded Graph Neural Networks

Olga Fink

Over the last decade, graph neural networks (GNNs) have made significant progress in numerous graph machine learning tasks. In real-world applications, where domain shifts occur and labels are often unavailable for a new target domain, graph domain adaptat ...

Association for the Advancement of Artificial Intelligence2025

Physics-Enhanced Graph Neural Networks for Soft Sensing in Industrial Internet of Things

Olga Fink, Keivan Faghih Niresi

The Industrial Internet of Things (IIoT) is reshaping manufacturing, industrial processes, and infrastructure management. By fostering new levels of automation, efficiency, and predictive maintenance, IIoT is transforming traditional industries into intell ...

2024

Robust time series denoising with learnable wavelet packet transform

Olga Fink, Gaëtan Michel Frusque

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

2024