Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction

arXiv:2404.06418v1 Announce Type: new
Abstract: Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional studies leveraging explainability methods to complement the previous experiments and further enhance the understanding of latent representations generated by the model. The adopted methods are general enough to be …

abstract arxiv continuous cs.ai cs.lg diverse fields impact network networks neural networks novel representation sampling scientific studying type work