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September 24, 2025Open Access

D3PINNs: A Novel Physics-Informed Neural Network Framework for Staged Solving of Time-Dependent Partial Differential Equations

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D3PINNs effectively solve time-dependent PDEs by dynamically capturing solutions, improving computational efficiency.
The framework uses domain decomposition and retains time derivative terms, simplifying PDEs to ordinary differential equations.
Numerical experiments support the efficacy of D3PINNs in addressing complex time-dependent problems in computational mathematics.
This innovative approach demonstrates flexibility and accuracy in modeling, showing promise for various scientific applications.

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Novel framework solves time-dependent partial differential equations using domain decomposition, indicating enhanced computational efficiency.

Yang et al. (2025) studied this question.

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Novel framework solves time-dependent partial differential equations using domain decomposition, indicating enhanced computational efficiency.