This paper is under CC 4.0 license. available on arxiv 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 & dlyoung@ntu.edu.tw; (3) Y.C. Hon, Department of Mathematics, City University of Hong Kong; (4) C.S. Chen, Department of Civil Engineering, National Taiwan University & dchen@ntu.edu.tw. Table of Links Abstract & Introduction Neural Networks PINN for Solving Inverse Burgers’ Equation Residual Network Numerical Results Results, Acknowledgments & References 3 PINN for Solving Inverse Burgers’ Equation 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.

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

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