A hybrid MLP-CNN model based on positional encoding for daytime radiative cooler

Abstract

Radiative cooling is a groundbreaking technology for sustainable temperature regulation to combat global warming and the urban heat island effect. Traditional optimization algorithms for cooling system structures are often brute-force and even limited to local optimal solutions. Here, we propose a joint simulation method based on a multilayer perceptron (MLP) and a convolutional neural network (CNN) with position encoding (PE), namely the hybrid MLP-CNN model with PE. This method can significantly reduce simulation time, avoid getting trapped in local optima, and accurately predict the optical response and radiative cooling power of the structure. Applying PE enables the MLP model to learn the relationship between structural parameters and optical response more quickly and effectively, reducing loss during training and more minor root mean square error (RMSE) and mean relative error (MRE) during testing. In addition, the residual network can reduce the problem of overfitting in the network. Compared to using only an MLP, the hybrid MLP-CNN with PE model can effectively reduce loss and achieve more accurate prediction outputs. The hybrid model is much faster than traditional electromagnetic simulations, taking only 1% of the time, and the model dramatically reduces the time needed to predict the optical response of structures and effectively decreases the cost of designing optical devices. Utilizing this hybrid model not only avoids the potential drawbacks of being limited to suboptimal solutions and significantly reduces the simulation time, but also effectively predicts the optical response of the device. This approach can demonstrate remarkable adaptability in optimizing geometric parameters of various optical apparatuses, including polarization converters, beam splitters, metalens, and other two-dimensional nanostructures.