國立中正大學數學系

暨應用數學碩士班、統計科學碩士班

學    術   演    講

Conservative Physics-informed Neural Networks for Generalized

Buckley-leverett Equation Near Critical States

Dr. Reyna Quita

國立台灣大學應用數學科學研究所

Institute of Applied Mathematical Sciences, National Taiwan University

Abstract

        In this talk, a modified version of conservative Physics-informed Neural Networks (cPINN) is provided to construct the solutions of Riemann problem for the hyperbolic scalar conservation laws in non-conservative form and scalar conservation laws with discontinuous perturbation in the flux. To demonstrate the results, we use the model of generalized Buckley-Leverett equation (GBL equation) with discontinuous porosity in porous media. By inventing a new unknown, the GBL equation is transformed into a two-by-two resonant hyperbolic conservation laws in conservative form. We experiment with our idea by using a cPINN algorithm to solve the GBL equation in both conservative and non-conservative forms, as well as the cases of critical and non-critical states. This method provides a combination of two different neural networks and corresponding loss functions, one is for the two-by-two resonant hyperbolic system, and the other is for the scalar conservation law with a discontinuous perturbation term in the non-convex flux. The technique of re-scaling to the unknowns is adopted to avoid the oscillation or inaccurate speed of the Riemann solutions in the cases of critical Riemann data. The solutions constructed by the modified cPINN match the exact solutions constructed by the theoretical analysis for hyperbolic conservation laws. Finally, we compare the performance of the modified cPINN with numerical finite difference method WENO5. Whereas WENO5 struggles with the approximate solutions for the Riemann problems of GBL equation in non-conservative form, cPINN works admirably.

Keywords: Physics-informed Neural Networks (PINN), cPINN, Deep Learning, Hyperbolic System of Conservation Laws, Generalized Buckley-Leverett Equation, Riemann Problems, Entropy Conditions.

日  期:113年10月16日(星期三) 16:10~17:00

地  點：本校數學館527教室（嘉義縣民雄鄉大學路168號）

茶  會：15:30~16:00數學館四樓409室舉行

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