We consider the use of Deep Learning methods for modeling complex phenomena like those occurring in natural physical processes. With the large amount of data gathered on these phenomena the data intensive paradigm could begin to challenge more traditional approaches elaborated over the years in fields like maths or physics. However, despite considerable successes in a variety of application domains, the machine learning field is not yet ready to handle the level of complexity required by such problems. Using an example application, namely Sea Surface Temperature Prediction, we show how general background knowledge gained from physics could be used as a guideline for designing efficient Deep Learning models. In order to motivate the approach and to assess its generality we demonstrate a formal link between the solution of a class of differential equations underlying a large family of physical phenomena and the proposed model. Experiments and comparison with series of baselines including a state of the art numerical approach is then provided.
Incorporating Domain Knowledge into Deep Neural Networks
APP Centre for Artificial Intelligence Research (APPCAIR)（人工智能研究中心）；Department of Electrical and Electronics Engineering（电子与电气工程系）；Department of Computer Science and Information Systems（计算机科学与信息系统系）；BITS Pilani, K.K. Birla Goa Campus（比斯汉学院，戈亚校区）
A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature
Chengdu Library and Information Center, Chinese Academy of Sciences（中国科学院成都文献情报中心）；University of Michigan（密歇根大学）；Shandong University of Technology（山东理工大学）；Kunming University of Science and Technology（昆明理工大学）；Macau University of Science and Technology（澳门科技大学）；Purdue University（普渡大学）
ChatSR: Multimodal Large Language Models for Scientific Formula Discovery
AnnLab, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China（安 lab，半导体研究所，中国科学院，北京，中国）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China（电子、电气与通信工程学院，中国科学院大学，北京，中国）；Zhongguancun Academy, Beijing, China（中关村学院，北京，中国）；School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing 101408, China（先进交叉科学学院，中国科学院大学，北京101408，中国）；College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing, 100049, China（材料科学与光电技术学院，中国科学院大学，北京100049，中国）；School of Integrated Circuits, University of Chinese Academy of Sciences, Beijing 100049, China（集成电路学院，中国科学院大学，北京100049，中国）
PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors
Center of Excellence for Generative AI, KAUST（生成人工智能卓越中心，KAUST）；The Swiss AI Lab, IDSIA-USI/SUPSI（瑞士人工智能实验室，IDSIA-USI/SUPSI）；NNAISENSE Principia-AI/PhysGym（NNAISENSE 原初人工智能/PhysGym）
The Chinese University of Hong Kong（香港中文大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Washington（华盛顿大学）；Allen Institute for AI（艾伦人工智能研究所）；Facebook AI Research（脸书人工智能研究院）
Distribird: Literature-Informed Prior Distribution Design for Bayesian Model Calibration
Obuda University（欧布达大学）；Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺依曼信息学院生物信息学与应用人工智能研究所）；Physiological Controls Research Center, Obuda University（欧布达大学生理控制研究中心）；HUN-REN Centre for Agricultural Research（HUN-REN农业研究中心）；Czech Academy of Sciences（捷克科学院）
