CAN-PINN: A fast physics-informed neural network based on coupled-automatic–numerical differentiation method
Agency for Science, Technology and Research · Nanyang Technological University
Indexed inarxivcrossref
Abstract
No abstract available for this paper.
Citation impact
315
total citations
- FWCI
- 33.14
- Percentile
- 100%
- References
- 97
Citations per year
Authors
5- PCPao‐Hsiung Chiu
Agency for Science, Technology and Research
- JCJian Cheng WongCorresponding
Agency for Science, Technology and Research, Nanyang Technological University
- CCChin Chun Ooi
Agency for Science, Technology and Research
- MHMy Ha Dao
Agency for Science, Technology and Research
- YOYew-Soon Ong
Agency for Science, Technology and Research, Nanyang Technological University
Topics & keywords
Topics
Keywords
- Automatic differentiation
- Artificial neural network
- Computer science
- Applied mathematics
- Physics
- Computational science
- Algorithm
- Artificial intelligence
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