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Physics-Informed with Power-Enhanced Residual Network: PINN for Solving Inverse Burgers’ Equation by@interpolation

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by The Interpolation PublicationFebruary 28th, 2024

This paper is available on arxiv under CC 4.0 license.

**Authors:**

(1) Amir Noorizadegan, Department of Civil Engineering, National Taiwan University;

(2) D.L. Young, Core Tech System Co. Ltd, Moldex3D, Department of Civil Engineering, National Taiwan University & [email protected];

(3) Y.C. Hon, Department of Mathematics, City University of Hong Kong;

(4) C.S. Chen, Department of Civil Engineering, National Taiwan University & [email protected].

PINN for Solving Inverse Burgers’ Equation

Results, Acknowledgments & References

In this section, we explore the application of Physics-Informed Neural Networks (PINN) [1] to solve the inverse Burgers’ equation in one dimension. The 1D Burgers’ equation is given by:

The PINNs loss function is given by (Fig. 1(II)):

We aim to minimize MSE to obtain the neural network parameters (w, bi) and the Burgers’ equation parameters λ1 and λ2.

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