FS24-Talk2: Prof.Dr. Olga Fink, Leveraging Inductive Bias for Physically Consistent Machine Learning

Abstract:
In the field of engineered systems, the integration of machine learning has enabled the development of advanced predictive models that ensure the reliable operation of complex assets. However, challenges such as sparse, noisy, and incomplete data necessitate the integration of prior knowledge and inductive bias to improve generalization, interpretability, and robustness.
Inductive bias, the set of assumptions embedded in machine learning models, plays a crucial role in guiding these models to generalize effectively from limited training data to real-world scenarios. In engineered systems, where physical laws and domain-specific knowledge are fundamental, the use of inductive bias can significantly enhance a model’s ability to predict system behavior under diverse operating conditions. By embedding physical principles into learning algorithms, inductive bias reduces the reliance on large datasets, ensures that model predictions are physically consistent, and enhances both the generalizability and interpretability of the models.
This talk will explore various forms of inductive bias applied in engineered systems, with a particular focus on heterogenous temporal, physics-informed and algorithm-informed graph neural networks with applications in virtual sensing, modelling multi-body dynamical systems and anomaly detection.
Short Bio:
Olga Fink has been assistant professor of Intelligent Maintenance and Operations Systems at EPFL since March 2022. Olga’s research focuses on Hybrid Algorithms Fusing Physics-Based Models and Deep Learning Algorithms, Hybrid Operational Digital Twins, Transfer Learning, Self-Supervised Learning, Deep Reinforcement Learning and Multi-Agent Systems for Intelligent Maintenance and Operations of Infrastructure and Complex Assets.
Before joining EPFL faculty, Olga was assistant professor of intelligent maintenance systems at ETH Zurich from 2018 to 2022, being awarded the prestigious professorship grant of the Swiss National Science Foundation (SNSF). Between 2014 and 2018 she was heading the research group “Smart Maintenance” at the Zurich University of Applied Sciences (ZHAW).
Olga received her Ph.D. degree from ETH Zurich with the thesis on “Failure and Degradation Prediction by Artificial Neural Networks: Applications to Railway Systems”, and Diploma degree in industrial engineering from Hamburg University of Technology. She has gained valuable industrial experience as reliability engineer with Stadler Bussnang AG and as reliability and maintenance expert with Pöyry Switzerland Ltd.
Olga has been a member of the BRIDGE Proof of Concept evaluation panel since 2023. Moreover, Olga is serving as an editorial board member of several prestigious journals, including Mechanical Systems and Signal Processing, Engineering Applications of Artificial Intelligence, Reliability Engineering and System Safety and IEEE Sensors Journal.
In 2018, Olga was honored as one of the “Top 100 Women in Business, Switzerland”. Additionally, in 2019, earned the distinction of being recognized as a young scientist of the World Economic Forum. In 2020 and 2021, she was honored as a young scientist of the World Laureate Forum. In 2023, she was distinguished as a fellow by the Prognostics and Health Management Society.