MPI Colloquia Series | Prof. Olga Fink, EPFL

The Max Planck Institute Magdeburg invites you to its series of colloquia.
Top-class scientists, from notable German and worldwide research institutions, give a survey of their research work.

Everybody who is interested, is invited to attend.

Abstract

Accurate and interpretable modeling of multi-body dynamical systems is a fundamental challenge in domains ranging from robotics and aerospace to biophysics and materials science. Traditional physics-based approaches are often computationally expensive and difficult to scale, while purely data-driven methods like graph neural networks (GNNs) may lack physical consistency and generalization. This talk presents Dynami-CAL GraphNet, a new physics-informed GNN framework that explicitly integrates conservation laws, specifically, the pairwise conservation of linear and angular momentum, into its architecture. By leveraging edge-local reference frames that are equivariant to rotations and translations, our model produces physically consistent predictions and offers interpretable insights into the forces and moments governing each interaction.

We demonstrate the effectiveness of Dynami-CAL GraphNet across a wide spectrum of tasks. Beyond standard 3D granular systems with inelastic collisions, we systematically evaluated the model on complex, real-world datasets, including human body motion prediction and protein molecular dynamics simulations. In all cases, Dynami-CAL GraphNet was benchmarked against several established baseline methods. Our results show not only stable error accumulation over extended prediction horizons and superior maintenance of physical constraints, but also a strong ability to extrapolate to previously unseen system configurations and interaction regimes, a key capability for robust deployment in real-world scenarios.

This talk will highlight how embedding physical principles within machine learning architectures enables not only accuracy and interpretability, but also robust extrapolation to previously unseen scenarios, opening new avenues for real-time, scalable, and generalizable modeling of complex systems in science and engineering.

Link to the paper: https://www.nature.com/articles/s41467-025-67802-5

About the speaker (Personal website)

Olga Fink has been assistant professor at EPFL since March 2022, heading the Intelligent Maintenance and Operations Systems (IMOS) laboratory. She is the recipient of an ERC Consolidator Grant. Olga’s research focuses on Physics-Informed Machine Learning, Multi-Modal Learning, Domain Adaptation and Generalization, and Reinforcement Learning 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, and Diploma degree 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 is serving as an editorial board member of several prestigious journals, including Mechanical Systems and Signal Processing, Engineering Applications of Artificial Intelligence and Reliability Engineering and System Safety.
In 2019, Olga earned the distinction of being recognized as a young scientist of the World Economic Forum. In 2020, 2021, and 2024 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.