Physics-informed Neural Networks approach to solve the Blasius function
Author:
Affiliation:
1. Amrita Vishwa Vidyapeetham,Department of Mathematics,Amritapuri,India
2. Amrita Vishwa Vidyapeetham,Department of Mechanical Engineering,Amritapuri,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10179605/10179606/10179704.pdf?arnumber=10179704
Reference33 articles.
1. Physics informed deep learning (part i): Data-driven so-lutions of nonlinear partial differential equations;raissi;ar Xiv preprint,2017
2. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
3. fPINNs: Fractional Physics-Informed Neural Networks
4. On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems
5. A new solution branch for the Blasius equation—A shrinking sheet problem
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