Inferring, Predicting, and Denoising Causal Wave Dynamics
This addresses the challenge of causal inference and prediction in complex spatio-temporal dynamics for applications such as brain imaging and weather data, representing a novel method for a known bottleneck.
The paper tackles the problem of modeling spatially distributed, non-linear dynamical processes by introducing DISTANA, a generative recurrent graph convolution neural network, which significantly outperforms alternatives like temporal convolution networks and ConvLSTMs on a wave propagation benchmark, producing stable predictions over hundreds of time steps and effectively filtering noise.
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, non-linear dynamical processes. We show that DISTANA is very well-suited to denoise data streams, given that re-occurring 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 spatio-temporal dynamics such as brain imaging, supply networks, water flow, or soil and weather data patterns.