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Discovery Science 2026: International Conference on Discovery Science

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投稿締切日:
2026-06-15 締切済み
通知日:
2026-07-21
開催日:
2026-10-05
開催地:
Mainz, Germany
開催回数:
ICORE: B   QUALIS: B1   閲覧: 202656   フォロー: 0   参加: 0
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Discovery Science
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会伴インデックス (CP-I)

65.1 / 100
全 5,759 件中 第 444 位 · 上位 8%
学術的評価 (35%)
78
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
92
コミュニティの注目度 (10%)
12
公開情報の充実度 (15%)
55

使用した入力: 収録ランク：ICORE B, QUALIS B1 · 確認できる開催回数：29 · 過去 24 か月にこのページを開いた研究者：4 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-10-24

論文募集

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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