most citedImpact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics

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cs.LG2025★ 2 cited

Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics

Yi En Chou, Te Hsin Liu, Chao-An Lin

Physics Informed Neural Networks offer a mesh free framework for solving PDEs but are highly sensitive to loss weight selection. We propose two dimensional analysis based weighting…

cs.LG2025

Multi-level datasets training method in Physics-Informed Neural Networks

Yao-Hsuan Tsai, Hsiao-Tung Juan, Pao-Hsiung Chiu +1

Physics-Informed Neural Networks have emerged as a promising methodology for solving PDEs, gaining significant attention in computer science and various physics-related fields. Des…