Inferring, Predicting, and Denoising Causal Wave Dynamics

The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally parameterizable laterally connected network modules. DISTANA is specifically designed to identify the causality behind spatially distributed, nonlinear dynamical processes. We show that DISTANA is very well-suited to denoise data streams, given that reoccurring patterns are observed, significantly outperforming alternative approaches, such as temporal convolution networks and ConvLSTMs, on a complex spatial wave propagation benchmark. It produces stable and accurate closed-loop predictions even over hundreds of time steps. Moreover, it is able to effectively filter noise-- an ability that can be improved further by applying denoising autoencoder principles or by actively tuning latent neural state activities retrospectively. Results confirm that DISTANA is ready to model real-world spatiotemporal dynamics such as brain imaging, supply networks, water flow, or soil and weather data patterns. Keywords: recurrent neural networks · temporal convolution · graph neural networks · distributed sensor mesh · noise filtering.