A Physics-Informed Neural Network Method for Solving Forward and Inverse Problems of Thermal-Humidity Coupling Model in Single-Layer Fabrics
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.
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