A Bibliometric Analysis of Physics-Based and Data-Driven Hybrid Modeling

Author:

Kasilingam Sathish,Keepers Makenzie,Wuest Thorsten

Funder

US Department of Energy

Publisher

Elsevier BV

Subject

General Medicine

Reference19 articles.

1. Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation;Lu;Computers in Industry;,1990

2. Development of specific grinding energy model;Singh;International Journal of Machine Tools and Manufacture;,2012

3. Singh SK, Yang R, Behjat A, Rai R, Chowdhury S, Matei I. PI-LSTM: Physics-infused long short-term memory network. IEEE International Conference on Machine Learning and Applications; 2019;34-41.

4. VOSviewer manual;Van Eck;Leiden: Univeristeit Leiden;,2013

5. Review of Voyant Tools;Welsh;Collaborative Librarianship;,2014

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Physics-based and data-driven hybrid modeling in manufacturing: a review;Production & Manufacturing Research;2024-01-18

2. Research on Smart Grid Dispatching Control System Based on Model Fusion;2022 5th International Conference on Power and Energy Applications (ICPEA);2022-11-18

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