Physics-informed Neural Networks approach to solve the Blasius function

• URL: http://arxiv.org/abs/2301.00106v1
• Date: Sat, 31 Dec 2022 03:14:42 GMT
• Title: Physics-informed Neural Networks approach to solve the Blasius function
• Authors: Greeshma Krishna, Malavika S Nair, Pramod P Nair, Anil Lal S
• Abstract summary: This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. It is seen that this method produces results that are at par with the numerical and conventional methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

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