Inferring, Predicting, and Denoising Causal Wave Dynamics

• URL: http://arxiv.org/abs/2009.09187v1
• Date: Sat, 19 Sep 2020 08:33:53 GMT
• Title: Inferring, Predicting, and Denoising Causal Wave Dynamics
• Authors: Matthias Karlbauer, Sebastian Otte, Hendrik P.A. Lensch, Thomas Scholten, Volker Wulfmeyer, and Martin V. Butz
• Abstract summary: The DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. We show that DISTANA is very well-suited to denoise data streams, given that re-occurring patterns are observed. It produces stable and accurate closed-loop predictions even over hundreds of time steps.
• Score: 3.9407250051441403
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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