共 31 条
- [31] Yildiz N.T., Kocaman H., Yildirim H., Canli M., An investigation of machine learning algorithms for prediction of temporomandibular disorders by using clinical parameters, Medicine (Baltimore), 103, 41, (2024)
Predictive stress analysis in simplified spinal disc model using physics-informed neural networks
被引：0
作者：
Kim, Kwang Hyeon
[1
]
Koo, Hae-Won
[2
]
Lee, Byung-Jou
[2
]
机构：
[1] Inje Univ, Ilsan Paik Hosp, Clin Res Support Ctr, Goyang, South Korea
[2] Inje Univ, Coll Med, Ilsan Paik Hosp, Dept Neurosurg, Goyang, South Korea
基金：
新加坡国家研究基金会;
关键词：
Physics-informed neural networks;
spinal disc modeling;
stress prediction;
biomechanics;
NUCLEUS PULPOSUS;
BIOMECHANICS;
BEHAVIOR;
D O I：
10.1080/10255842.2025.2471504
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ;
0835 ;
摘要：
This study develops a physics-informed neural network (PINN) model to predict stress distribution in a simplified spinal disc structure. The model incorporates 3D spatial inputs and enforces equilibrium conditions through a custom loss function. Trained on synthetic elasticity-based data, it achieves an MAE of 0.026 and an R-2 of 74.6%. Stress patterns under various loading conditions were visualized, with peak stress occurring at z = 1 under top compression. Results demonstrate PINNs' potential for biomechanical modeling, improving predictive accuracy in spinal biomechanics and informing clinical interventions.
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页数：13
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