September 18 – 23 , 2022, Dagstuhl Seminar 22382

Machine Learning for Science: Bridging Data-driven and Mechanistic Modelling


Philipp Berens (Universität Tübingen, DE)
Kyle Cranmer (University of Wisconsin – Madison, US)
Neil D. Lawrence (University of Cambridge, GB)
Ulrike von Luxburg (Universität Tübingen, DE)


Jessica Montgomery (University of Cambridge, GB)

For support, please contact

Susanne Bach-Bernhard for administrative matters

Michael Gerke for scientific matters

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Machine learning has the potential to transform research and innovation. Today’s machine learning methods are already being applied to advance the frontiers of science, helping researchers better understand how the world around us works – from interactions between atoms, to the ways that proteins fold, interactions between cells, the dynamics of Earth’s systems and the discovery of exoplanets. These contributions are the foothills of the wider transformation that machine learning could bring for science and the scientific workflow.

Recent successes in the deployment of machine learning for scientific discovery point to the potential of a new generation of machine learning methods for science. These tools would combine data-derived insights with existing domain knowledge or theory, creating more powerful analytical tools. They would enhance researchers’ ability to simulate the systems they study, testing new ideas or identifying new areas for investigation; and they would support researchers to understand not only what patterns can be found in data, but why and how such patterns have emerged.

Creating this new generation of machine learning methods requires further efforts to bridge the current gap between data-driven and mechanistic modelling. Recent successes in the field suggest a route to create these hybrid approaches. Through further development of machine learning approaches that encode domain knowledge in data-driven systems, that enable simulation and emulation of complex real-world systems, and that allow causal inference in data-enabled systems, machine learning research could create more powerful tools for scientific discovery.

This Dagstuhl Seminar will seek to articulate a roadmap for bridging the gap between data-driven and mechanistic modelling approaches. It will consider the lessons that recent work at the interface of machine learning and science provides for the future development of the field, and it will review emerging research directions at this interface. In so doing, it will identify a set of common interests where further research could unlock progress in the use of machine learning for scientific discovery.

Machine learning methods have already been successfully adopted in a variety of scientific domains. This seminar will review recent experiences of – and lessons learned from – efforts to deploy machine learning to advance:

  • Healthcare and biomedical sciences including neuroscience
  • Climatology and environmental sciences
  • Theoretical and experimental physics

By reviewing these recent experiences, the seminar will identify emerging research directions and best practices in:

  • Encoding domain knowledge in machine learning systems, reviewing methods for leveraging insights from data while embedding the knowledge contained in mechanistic modelling approaches.
  • Simulation and emulation, investigating how innovations in the mathematics of emulation and techniques for understanding uncertainty propagation can support more effective machine learning tool.
  • Approaches to causality in machine learning, exploring how techniques from statistical inference and uncertainty quantification can be combined to create a new mathematics of causality.

Motivation text license
  Creative Commons BY 4.0
  Philipp Berens, Kyle Cranmer, Neil D. Lawrence, Jessica Montgomery, and Ulrike von Luxburg


  • Artificial Intelligence
  • Machine Learning


  • Machine learning
  • AI
  • Scientific discovery


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


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