| 摘 要: 针对织物热湿耦合模型难以解耦和反问题求解时间长的问题,提出了一种求解单层稳态织物热湿耦合 传递模型正反问题的物理信息神经网络(Physics-Informed Neural Networks,PINNs)方法。首先,给出了求解单层 织物热湿传递方程正问题的PINNs方法,并采用数值实验验证了方法的有效性。其次,提出了基于热湿舒适性的 厚度参数决定反问题,并使用PINNs方法进行求解。数值实验结果显示,PINNs方法在求解参数决定反问题时,仅 需5 min即可预测出概率函数,相比于微分方程数值求解和粒子群结合方法,求解效率提高了25倍,展现出显著的 优越性和应用潜力。 |
| 关键词: 单层织物;热湿模型;耦合方程;神经网络;PINNs TP183 |
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中图分类号: TP183
文献标识码: A
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| A Physics-Informed Neural Network Method for Solving Forward and Inverse Problems of Thermal-Humidity Coupling Model in Single-Layer Fabrics |
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CAI Qifan1, XU Yinghong1,2
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(1.School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China;
2.School of Science, Zhejiang Sci-Tech University, Hangzhou 310018, China)
1434507771@qq.com; xyh7913@zstu.edu.cn
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| Abstract: In response to the challenges of decoupling the thermal-humidity coupling model of fabrics and the long solution time of inverse problems, this paper proposes a Physics-Informed Neural Networks (PINNs) method for solving the forward and inverse problems of the steady-state thermal-humidity coupling transfer model of single-layer fabrics. First, the PINNs approach is presented for solving the forward problem of the thermal-humidity transfer equation for single-layer fabrics, and numerical experiments are conducted to validate the effectiveness of the method. Next, an inverse problem determined by thickness parameters is proposed based on thermal-humidity comfort, which is solved using the PINNs method. The numerical experiment results show that the PINNs method can predict the probability function in just 5 minutes when solving the parameter-determined inverse problem, achieving a 25-fold increase in computational efficiency, compared to traditional numerical solutions of differential equations combined with particle swarm optimization methods, demonstrating significant advantages and application potential. |
| Keywords: single-layer fabrics; thermal-humidity model; coupling equations; neural network; PINN |
