About the workshop

Scientific Machine Learning (SciML), the fusion of scientific computing and machine learning algorithms, has proven to be a transformative force in academia and is rapidly establishing a firm foothold in industrial applications. A foundational 2018 workshop outlined six priority research directions (PRDs) for the nascent field of SciML, anchoring future development around critical pillars such as domain awareness, robustness, and interpretability.
The SciML landscape has evolved profoundly over the past seven years. Considering these advancements, this workshop, hosted by the Center of Advanced Analytics and Predictive Sciences at the University of Augsburg, convenes researchers from diverse disciplines to re-evaluate the original priorities within the context of current technological frontiers. Through expert keynotes, technical presentations, and collaborative breakout sessions, participants will share practical insights and critically analyze current progress and emerging challenges within the field. Our primary objective is to assess the trajectory of the original PRDs and collaboratively shape the future research agenda for SciML.
The insights generated during this two-day event will be synthesized into a comprehensive scientific article, targeting high-impact journals such as Nature Machine Intelligence or Neurocomputing.

Further information

The event is organised by Prof. Lars Mikelsons, Prof. Michael Schlottke-Lakemper, and Andreas Hofmann, and hosted by the Centre for Advanced Analytics and Predictive Sciences at the University of Augsburg.
Further details are available at: https://una-auxme.github.io/SciMLFrontiers_WS2026/