mlsanityData to 13 Oct 2026

AI for Science: An Emerging Agenda

arXiv 2303.04217 ↗First posted 7 Mar 2023cs.AI cs.CYcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). Builds on 30 of its 80 references that are in the map. First posted on arXiv 7 Mar 2023.

Builds on

30 of 80 references.

References · 30

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18 more
  1. 2021A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithfulcited by 28
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Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 3.

5 papers.

  1. 2023The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligencecited by 12
  2. 2026Autonomous Agents Coordinating Distributed Discovery Through Emergent Artifact Exchangecited by 11
  3. 2026BeamPERL: Parameter-Efficient RL with Verifiable Rewards Specializes Compact LLMs for Structured Beam Mechanics Reasoningcited by 4
  4. 2025Recent Advances in Simulation-based Inference for Gravitational Wave Data Analysiscited by 0
  5. 2026Cross-domain benchmarks reveal when coordinated AI agents improve scientific inference from partial evidencecited by 0