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physics.comp-ph2025
Gradient-enhanced PINN with residual unit for studying forward-inverse problems of variable coefficient equations
Hui-Juan Zhou, Yong Chen
Physics-informed neural network (PINN) is a powerful emerging method for studying forward-inverse problems of partial differential equations (PDEs), even from limited sample data.…
physics.comp-ph2024★ 1 cited
Causality-guided adaptive sampling method for physics-informed neural networks
Shuning Lin, Yong Chen
Compared to purely data-driven methods, a key feature of physics-informed neural networks (PINNs) - a proven powerful tool for solving partial differential equations (PDEs) - is th…
physics.comp-ph2023★ 1 cited
The improved backward compatible physics-informed neural networks for reducing error accumulation and applications in data-driven higher-order rogue waves
Shuning Lin, Yong Chen
Due to the dynamic characteristics of instantaneity and steepness, employing domain decomposition techniques for simulating rogue wave solutions is highly appropriate. Wherein, the…