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Disentangled Generative Models for Robust Prediction of System Dynamics

Stathi Fotiadis · Mario Lino · Shunlong Hu · Stef Garasto · Chris Cantwell · Anil Bharath

Exhibit Hall 1 #806
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The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this study, we address these challenges by considering the parameters of dynamical systems as factors of variation of the data and leverage their ground-truth values to disentangle the representations learned by generative models. Our experimental results in phase-space and observation-space dynamics, demonstrate the effectiveness of latent-space supervision in producing disentangled representations, leading to improved long-term prediction accuracy and out-of-distribution robustness.

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