论文标题
具有修改物理知识的神经网络（PINNS）的Allen-Cahn-Ohta-Kawasaki（ACOK）方程的数值近似值
Numerical Approximations of the Allen-Cahn-Ohta-Kawasaki (ACOK) Equation with Modified Physics Informed Neural Networks (PINNs)
论文作者
论文摘要
物理知情的神经网络（PINN）已被广泛用于数值近似PDE问题。尽管Pinns在为许多部分微分方程生产解决方案方面取得了良好的结果，但研究表明，它在相位场模型上的表现不佳。在本文中，我们通过引入修改后的物理知识神经网络来部分解决此问题。特别是，它们用于数值近似于Allen-Cahn-Ohta-Kawasaki（ACOK）方程，并具有体积约束。
The physics informed neural networks (PINNs) has been widely utilized to numerically approximate PDE problems. While PINNs has achieved good results in producing solutions for many partial differential equations, studies have shown that it does not perform well on phase field models. In this paper, we partially address this issue by introducing a modified physics informed neural networks. In particular, they are used to numerically approximate Allen-Cahn-Ohta-Kawasaki (ACOK) equation with a volume constraint.
