... further complicate these problems, because temperatures can only be acquired from sparse and noisy measurements. Proper orthogonal decomposition (POD) and deep neural network auto-encoder (AE) are two typical reduced-order models to reconstruct the global temperature fields from sparse data points...

... methodologies often exhibit limitations such as high computational demands and diminished efficiency, particularly for complex scenarios. This study demonstrates the application of deep learning surrogate models based on the feedforward neural network (FNN) to optimize heat source layouts. These models provide...