Robust discovery of partial differential equations in complex situations

• URL: http://arxiv.org/abs/2106.00008v1
• Date: Mon, 31 May 2021 02:11:59 GMT
• Title: Robust discovery of partial differential equations in complex situations
• Authors: Hao Xu and Dongxiao Zhang
• Abstract summary: A robust deep learning-genetic algorithm (R-DLGA) that incorporates the physics-informed neural network (PINN) is proposed in this work. The stability and accuracy of the proposed R-DLGA in several complex situations are examined for proof-and-concept. Results prove that the proposed framework is able to calculate derivatives accurately with the optimization of PINN.
• Score: 3.7314701799132686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven discovery of partial differential equations (PDEs) has achieved considerable development in recent years. Several aspects of problems have been resolved by sparse regression-based and neural network-based methods. However, the performances of existing methods lack stability when dealing with complex situations, including sparse data with high noise, high-order derivatives and shock waves, which bring obstacles to calculating derivatives accurately. Therefore, a robust PDE discovery framework, called the robust deep learning-genetic algorithm (R-DLGA), that incorporates the physics-informed neural network (PINN), is proposed in this work. In the framework, a preliminary result of potential terms provided by the deep learning-genetic algorithm is added into the loss function of the PINN as physical constraints to improve the accuracy of derivative calculation. It assists to optimize the preliminary result and obtain the ultimately discovered PDE by eliminating the error compensation terms. The stability and accuracy of the proposed R-DLGA in several complex situations are examined for proof-and-concept, and the results prove that the proposed framework is able to calculate derivatives accurately with the optimization of PINN and possesses surprising robustness to complex situations, including sparse data with high noise, high-order derivatives, and shock waves.

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