In this paper, we propose a novel framework, Dynamic Domain Decomposition Physics-Informed Neural Networks (D3PINNs), for solving time-dependent partial differential equations (PDEs). In this framework, solutions of time-dependent PDEs are dynamically captured. First, an approximate solution is obtained by the Physics-Informed Neural Networks (PINNs) containing the domain decomposition, then the time derivative terms in the PDE will be retained and the other terms associated with the solution will be replaced with the approximate solution. As a result, the PDE reduces to an ordinary differential equations (ODEs). Finally, the time-varying solution will be solved by the classical numerical methods for ODEs. D3PINNs retain the computational efffciency and ffexibility inherent to PINNs and enhance the ability for capturing solutions of time-dependent PDEs. Numerical experiments validate the effectiveness of the proposed methods.
Yang et al. (Thu,) studied this question.