The Synergy of Scientific and Machine Learning Modeling Workshop (“SynS & ML”) is an interdisciplinary forum for researchers and practitioners interested in the challenges of combining scientific and machine-learning models. The goal of the workshop is to gather together machine learning researchers eager to include scientific models into their pipelines, domain experts working on augmenting their scientific models with machine learning, and researchers looking for opportunities to incorporate ML in widely-used scientific models.

The power of machine learning (ML), its ability to build models by leveraging real-world data is also a big limitation; the quality and quantity of training data bound the validity domain of ML models. On the other hand, expert models are designed from first principles or experiences and labelled scientific if validated on curated real-world data, often even harvested for this specific purpose, as advised by the scientific method since Galileo. Expert models only describe idealized versions of the world which may hinder their deployment for important tasks such as accurate forecasting or parameter inference. This workshop focuses on the combination of two modelling paradigms: scientific and ML modelling. Sometimes called hybrid learning or grey-box modelling, this combination should 1) unlock new applications for expert models, and 2) leverage the data compressed within scientific models to improve the quality of modern ML models. In this spirit, the workshop focuses on the symbiosis between these two complementary modelling approaches; it aims to be a “rendezvous” between the involved communities, spanning sub-fields of science, engineering and health, and encompassing ML, to allow them to present their respective problems and solutions and foster new collaborations. The workshop invites researchers to contribute to such topics; see Call for Papers and Call for Scientific Models for more details.