Can A Neural Network Hear the Shape of A Drum?
This work addresses the inverse spectral problem in geometry, which is a domain-specific challenge with potential applications in fields like physics and engineering, though it appears incremental as it applies existing neural network methods to a new dataset.
The researchers tackled the problem of reconstructing the shape of polygonal domains from Laplacian eigenvalues using a deep neural network, achieving high prediction accuracy and demonstrating that the network recovers continuous rotational degrees of freedom and correlates latent variables with geometric parameters like area and perimeter.
We have developed a deep neural network that reconstructs the shape of a polygonal domain given the first hundred of its Laplacian eigenvalues. Having an encoder-decoder structure, the network maps input spectra to a latent space and then predicts the discretized image of the domain on a square grid. We tested this network on randomly generated pentagons. The prediction accuracy is high and the predictions obey the Laplacian scaling rule. The network recovers the continuous rotational degree of freedom beyond the symmetry of the grid. The variation of the latent variables under the scaling transformation shows they are strongly correlated with Weyl' s parameters (area, perimeter, and a certain function of the angles) of the test polygons.