A Physics Informed Neural Network for Time-Dependent Nonlinear and Higher Order Partial Differential Equations

Abstract

A physics informed neural network (PINN) incorporates the physics of a system by satisfying its boundary value problem through a neural network's loss function. The PINN approach has shown great success in approximating the map between the solution of a partial differential equation (PDE) and its spatio-temporal input. However, for strongly non-linear and higher order partial differential equations PINN's accuracy reduces significantly. To resolve this problem, we propose a novel PINN scheme that solves the PDE sequentially over successive time segments using a single neural network. The key idea is to re-train the same neural network for solving the PDE over successive time segments while satisfying the already obtained solution for all previous time segments. Thus it is named as backward compatible PINN (bc-PINN). To illustrate the advantages of bc-PINN, we have used the Cahn Hilliard and Allen Cahn equations, which are widely used to describe phase separation and reaction diffusion systems. Our results show significant improvement in accuracy over the PINN method while using a smaller number of collocation points. Additionally, we have shown that using the phase space technique for a higher order PDE could further improve the accuracy and efficiency of the bc-PINN scheme.

References in corpus (6)

• Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
• B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data
• Fourier Neural Operator for Parametric Partial Differential Equations
• Inferring solutions of differential equations using noisy multi-fidelity data
• Towards a 'Thermodynamics' of Active Matter
• Linking Machine Learning with Multiscale Numerics: Data-Driven Discovery of Homogenized Equations

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