# Physics-Informed Neural Networks: An AI Framework for Structural Failure Prediction of Rotating Disks > This article introduces a physics-informed AI framework that combines classical mechanics and machine learning for predicting structural failure of rotating disk systems, demonstrating the advantages of integrating physical constraints with data-driven methods. - 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo) - 发布时间: 2026-05-29T18:45:53.000Z - 最近活动: 2026-05-29T18:54:14.224Z - 热度: 150.9 - 关键词: 物理信息神经网络, PINN, 旋转圆盘, 结构失效预测, 机器学习, 经典力学, 工程AI, 科学计算 - 页面链接: https://www.zingnex.cn/en/forum/thread/ai-f30c8e9b - Canonical: https://www.zingnex.cn/forum/thread/ai-f30c8e9b - Markdown 来源: floors_fallback --- ## [Introduction] Physics-Informed Neural Networks: An Innovative AI Framework for Structural Failure Prediction of Rotating Disks This article introduces the physics-informed AI framework project released by Abhaya Kanwarthakur on GitHub, which combines classical mechanics and machine learning to predict structural failure of rotating disk systems. The framework integrates physical constraints and data-driven methods via Physics-Informed Neural Networks (PINN), addressing the limitations of traditional prediction methods (finite element analysis, empirical formulas) and holding significant application value in fields like aero-engines and wind power. ## Project Background: Engineering Pain Points in Rotating Disk Failure Prediction and Limitations of Traditional Methods Rotating machinery is core equipment in modern industry (e.g., aero turbine disks, wind turbine blades). When rotating at high speeds, they bear enormous centrifugal forces, and failure can lead to catastrophic consequences. Traditional prediction methods have limitations: finite element analysis has high computational costs and is difficult to apply in real time; empirical formulas have narrow applicability and insufficiently describe complex geometries and materials. This project proposes a physics-informed AI framework that integrates classical mechanics and machine learning to solve this problem. ## Core Methods: Principles and Technical Framework of Physics-Informed Neural Networks (PINN) ### Core Principles of PINN PINN embeds physical laws as constraints into training. The loss function includes a data fitting term (L_data) and a physical residual term (L_physics): L_total = L_data + λ*L_physics. It has data efficiency, physical consistency, extrapolation capability, and interpretability. ### Project Technical Framework - **Network Architecture**: Inputs are spatial coordinates (r, θ) and rotational speed ω; outputs are stress/displacement. Hidden layers use tanh or sin activation functions to facilitate high-order derivative calculation. - **Physical Constraints**: Construct residuals of control equations via automatic differentiation, and embed boundary conditions as hard/soft constraints. - **Data Fusion**: Combine simulation data, experimental data, and prior knowledge. - **Failure Prediction**: After predicting the stress field, apply failure criteria (maximum principal stress, von Mises, etc.) to calculate safety factors. ## Physical Mechanisms and Training Challenges: Stress Analysis of Rotating Disks and PINN Training Difficulties ### Physical Mechanisms of Rotating Disk Failure - **Stress Analysis**: Radial/circumferential stresses satisfy the equilibrium equations of elasticity, with boundary conditions of finite central stress and given radial stress at the outer edge. - **Failure Criteria**: Maximum principal stress, von Mises (equivalent stress ≥ yield strength), fatigue criteria, etc. - **Material Nonlinearity**: Plastic deformation, creep, and damage accumulation increase analytical difficulty. ### PINN Training Challenges - **Multi-task Loss Balance**: Need to dynamically adjust the weight between data and physical constraint losses. - **High-frequency Mode Capture**: Adaptive sampling or Fourier feature embedding is required for stress concentration areas. - **Convergence Stability**: High-order derivatives easily lead to gradient problems, requiring optimization of initialization and normalization. ## Application Scenarios: Practical Value of PINN in Aviation, Wind Power, and Other Fields The PINN framework can be applied in multiple fields: - **Aero-engines**: Real-time stress assessment of turbine disks, prediction of fatigue crack life, optimization of cooling hole design. - **Wind Power**: Monitoring blade health, predicting failure risks under extreme wind conditions, guiding maintenance plans. - **Energy Storage Flywheels**: Designing lightweight rotors, evaluating fault safety boundaries, optimizing magnetic bearing control. - **Manufacturing Processes**: Real-time prediction of rotational machining deformation for online compensation. ## Cutting-edge Trends: Extended Applications of PINN and Integration with Digital Twins ### Extended Applications PINN is expanding to fields such as fluid-structure interaction, multi-physics (thermal-mechanical-electrical), stochastic PINN (uncertainty), and spatiotemporal PINN (time-dependent phenomena). ### Integration with Digital Twins Physical constraints ensure the stability of twin models, while data-driven methods learn the characteristics of real systems, supporting online monitoring and prediction. ### Open-source Ecosystem Active open-source frameworks like DeepXDE, NeuroDiffEq, and SimNet lower the application threshold and promote technology popularization. ## Conclusion and Outlook: Physics-Informed AI as a Bridge Connecting Theory and Experiments This project demonstrates the organic combination of domain knowledge and data-driven methods. The PINN framework maintains physical interpretability while being flexible in rotating disk failure prediction. Such projects represent the cutting-edge direction of AI for Science. With the improvement of computing power and algorithm refinement, physics-informed AI is expected to become a bridge connecting theoretical models and experimental data, playing a role in more scientific and engineering fields.