会议信息

Discovery Science 2026: International Conference on Discovery Science

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截稿日期:
2026-06-15 已截稿
通知日期:
2026-07-21
会议日期:
2026-10-05
会议地点:
Mainz, Germany
届数:
ICORE: B   QUALIS: B1   浏览: 202658   关注: 0   参加: 0
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Discovery Science
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会伴指数 (CP-I)

65.1 / 100
全站第 444 名 / 共 5,759 个会议 · 前 8%
学术认可 (35%)
78
投稿选择性 (20%) 无数据 —— 按中性基准 50 分计入 —
会议传承 (20%)
92
社区关注 (10%)
12
资料公开度 (15%)
55

用到的输入: 收录等级：ICORE B, QUALIS B1 · 有据可查的届次：29 · 过去 24 个月打开过本页的研究者：4 人

公开资料里还缺: 历年录用率 (+4.5) · 最佳论文记录 (+2.3)
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置信度 80% —— 分数中有多大比例来自实际观测到的数据，而不是中性基准。 这个分数是怎么算出来的 · 查看完整榜单 · 算法版本 1.2 · 算于 2026-10-24

征稿

Discovery Science 2026 (International Conference on Discovery Science) is a ICORE B / QUALIS B1 conference held in Mainz, Germany on 2026-10-05. The paper submission deadline is 2026-06-15 (extended). Acceptance notifications are sent on 2026-07-21.

We invite submissions to the 29th International Conference on Discovery Science, welcoming paper submissions from all areas relevant to artificial intelligence and data science. The Discovery Science 2026 conference provides an open forum for intensive discussions and exchange of new ideas among researchers working in different areas of Artificial Intelligence and the data sciences, focusing on discovering and advancing scientific knowledge. Its scope includes developing and analyzing methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, and big data analytics, as well as their application in various domains of physical, life, environmental, natural and social sciences. This year, we particularly invite submissions dealing with methodological contributions to the automation or partial automation of discovery in various domains of the natural and human sciences, from a methodological perspective. Discovery Science 2026 is part of the AI4Science Week and co-located with the 2nd International Conference on Artificial Intelligence for Science (AI4Sci 2026), which features separate AI4Physics, AI4Chemistry&Materials, AI4LifeSciences, and AI4Humanities&SocialSciences tracks dealing with applications of AI and Machine Learning. You are welcome to submit abstracts in any of those tracks as well, especially if your contribution focuses on the application of existing techniques to a specific field of scientific inquiry. Contrary to the main track of Discovery Science 2026, note that AI4Sci tracks are non-archival and also suited to ongoing or already published work, similarly to workshops co-located with other major conferences. Aims & Scope The current call for papers is tentative, and will be updated during the year. This year's conference focuses on the following topics: Automated and semi-automated applications of Machine Learning and Artificial Intelligence in the discovery of scientific knowledge Benchmarking methodologies for the discovery of scientific knowledge Agentic AI methods and applications in scientific discovery Retrieval-Augmented Generation methods for the automation, both partial and full, of the scientific process Hypothesis generation methodologies, both causal and observational Causal discovery for the scientific process Methods for the integration of Machine Learning and AI in scientific laboratories, both at the hardware and software level Computational equation discovery and Symbolic Regression Methods and applications for safe and trustworthy AI (e.g. private, fair, transparent, sustainable, efficient) in the sciences, esp. social sciences and humanities While the intended emphasis of the conference is on the above topics, we also value and welcome contributions dealing with more general Machine Learning, Data Mining and AI topics: Machine Learning, including supervised learning, unsupervised learning, semi-supervised learning, self-supervised learning Active learning, online learning, transfer learning, continual learning etc. Reinforcement learning AutoML, Meta-Learning, Planning to Learn Representation learning for vision, text, audio, language, and other data modalities Knowledge Discovery and Data Mining Anomaly and Outlier Detection Causal Modeling and reasoning Neuro-symbolic learning & hybrid AI systems (logic & formal reasoning, etc.) Physics-informed machine learning Data and Knowledge Visualization Explainable AI and Interpretable Machine Learning Human-Machine Interaction for Knowledge Discovery and Management AI and High-performance Computing, Grid and Cloud Computing Optimisation AI Creativity Learning from Complex Data Data Streams, Evolving Data, Change Detection & Concept drift Time-Series Analysis Spatial, Temporal and Spatio-temporal Data Analysis Unstructured Data Analysis (textual and web data) Learning on graphs and topological deep learning Complex Network Analysis Process Discovery and Analysis Evaluation of Models and Predictions in Discovery Setting Applications of the above techniques in scientific domains, such as physical sciences (e.g., materials sciences, particle physics), life sciences (e.g., biology, medicine, neuroscience etc.), environmental sciences, natural and social sciences.
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