2026-08-10T16:16:47Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:17812 2026-04-20T13:18:30Z ddc:004 open_access

Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382) Berens, Philipp Cranmer, Kyle Lawrence, Neil D. von Luxburg, Ulrike Montgomery, Jessica machine learning artificial intelligence life sciences physical sciences environmental sciences simulation causality modelling This report documents the programme and the outcomes of Dagstuhl Seminar 22382 "Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling". Today’s scientific challenges are characterised by complexity. Interconnected natural, technological, and human systems are influenced by forces acting across time- and spatial-scales, resulting in complex interactions and emergent behaviours. Understanding these phenomena - and leveraging scientific advances to deliver innovative solutions to improve society’s health, wealth, and well-being - requires new ways of analysing complex systems. The transformative potential of AI stems from its widespread applicability across disciplines, and will only be achieved through integration across research domains. AI for science is a rendezvous point. It brings together expertise from AI and application domains; combines modelling knowledge with engineering know-how; and relies on collaboration across disciplines and between humans and machines. Alongside technical advances, the next wave of progress in the field will come from building a community of machine learning researchers, domain experts, citizen scientists, and engineers working together to design and deploy effective AI tools. This report summarises the discussions from the seminar and provides a roadmap to suggest how different communities can collaborate to deliver a new wave of progress in AI and its application for scientific discovery. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Philipp Berens and Kyle Cranmer and Neil D. Lawrence and Ulrike von Luxburg and Jessica Montgomery 2023 Is Part Of Dagstuhl Reports, Volume 12, Issue 9 (2023) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.12.9.150 urn:nbn:de:0030-drops-178125 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.9.150 eng https://creativecommons.org/licenses/by/4.0/legalcode