Disentangled Generative Models for Robust Prediction of System Dynamics
Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D Cantwell, Anil Anthony Bharath
Abstract
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.
BibTeX
@inproceedings{icml2023_disentangledgene,
title = {Disentangled Generative Models for Robust Prediction of System Dynamics},
author = {Stathi Fotiadis and Mario Lino Valencia and Shunlong Hu and Stef Garasto and Chris D Cantwell and Anil Anthony Bharath},
booktitle = {ICML 2023},
year = {2023}
}