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

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

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