AI Hopes and Fears
7 November 2023
Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling
24 August 2023
From 11 - 16 June 2023, 40 participants with expertise in applications of machine learning in the sciences took part in the workshop Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling, co-organised by Neil Lawrence and Jessica Montgomery from the Accelerate Programme and Bernhard Schölkopf (University of Tübingen). It set out to consider how mathematical innovations can help produce machine learning tools that can be deployed in support of scientific discovery, creating new interfaces between physical and data-driven modelling approaches.
In support of this objective, the workshop convened three discussion themes - Lessons from the application of machine learning in science; Foundational concepts and emerging methods; Machine learning for Earth and climate science.
A report summarising discussions and insights from the workshop can be accessed here.
An interactive graphic summary of the talks is available here.