Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks-Reference-Cited by-同舟云学术

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Self-Scalable Tanh (Stan): Multi-Scale Solutions for Physics-Informed Neural Networks

Published:2023-12 Issue:12 Volume:45 Page:15588-15603
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Gnanasambandam Raghav¹^ORCID,Shen Bo²^ORCID,Chung Jihoon¹^ORCID,Yue Xubo³^ORCID,Kong Zhenyu¹^ORCID

Affiliation:

1. Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA

2. Department of Mechanical and Industrial Engineering, New Jersey Institute of Technology, Newark, NJ, USA

3. Department of Mechanical & Industrial Engineering, Northeastern University, Boston, MA, USA

Funder

Office of Naval Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/10308548/10227556.pdf?arnumber=10227556

Reference53 articles.

1. An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications

2. Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

3. Prediction of aerodynamic flow fields using convolutional neural networks

4. Tackling Climate Change with Machine Learning

5. SciPy 1.0: fundamental algorithms for scientific computing in Python

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1. Adaptive activation functions for predictive modeling with sparse experimental data;Neural Computing and Applications;2024-07-24

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