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September 10, 2025Acta Physica SinicaOpen Access

allaPINNs: A physics-informed neural network with improvement of information representation and loss optimization for solving partial differential equations

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This framework improves predictive accuracy and feature extraction in PDE solutions using efficient loss optimization.

allaPINNs shows enhanced predictive accuracy for solving partial differential equations, improving by one to two orders of magnitude.
The improved architecture utilizes efficient linear attention to better capture key features and reduce complexity.
An augmented Lagrangian function is introduced, enhancing loss optimization and enabling effective training regulation.
Four benchmark equations were used to validate allaPINNs, demonstrating strong performance across varying complexities.

Zhang et al. (2025) studied this question.

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