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 $\mathrm{AI}$ 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.

翻译：本报告记录了达格斯图尔研讨会22382“面向科学的机器学习：弥合数据驱动与机理建模”的议程和成果。当今的科学挑战以复杂性为特征。相互关联的自然、技术和人类系统受到跨时间和空间尺度作用力的影响，导致复杂的交互和涌现行为。理解这些现象——并利用科学进步提供创新解决方案以改善社会健康、财富和福祉——需要分析复杂系统的新方法。人工智能的变革潜力源于其在各学科中的广泛适用性，且只有通过研究领域的整合才能实现。面向科学的人工智能是一个交汇点。它汇聚了人工智能与应用领域的专业知识；结合了建模知识与工程实践；依赖于跨学科以及人与机器之间的协作。除了技术进步，该领域的下一波进展将来自构建一个由机器学习研究人员、领域专家、公民科学家和工程师组成的社区，共同设计和部署有效的人工智能工具。本报告总结了研讨会上的讨论，并提供了路线图，以建议不同社区如何协作，推动人工智能及其在科学发现中的应用取得新进展。