Disentangled Generative Models for Robust Prediction of System Dynamics

• URL: http://arxiv.org/abs/2108.11684v3
• Date: Thu, 1 Jun 2023 10:32:38 GMT
• Title: Disentangled Generative Models for Robust Prediction of System Dynamics
• Authors: Stathi Fotiadis, Mario Lino, Shunlong Hu, Stef Garasto, Chris D Cantwell, Anil Anthony Bharath
• Abstract summary: In this work, we treat the domain parameters of dynamical systems as factors of variation of the data generating process. By leveraging ideas from supervised disentanglement and causal factorization, we aim to separate the domain parameters from the dynamics in the latent space of generative models. Results indicate that disentangled VAEs adapt better to domain parameters spaces that were not present in the training data.
• Score: 2.6424064030995957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks have become increasingly of interest in dynamical system prediction, but out-of-distribution generalization and long-term stability still remains challenging. In this work, we treat the domain parameters of dynamical systems as factors of variation of the data generating process. By leveraging ideas from supervised disentanglement and causal factorization, we aim to separate the domain parameters from the dynamics in the latent space of generative models. In our experiments we model dynamics both in phase space and in video sequences and conduct rigorous OOD evaluations. Results indicate that disentangled VAEs adapt better to domain parameters spaces that were not present in the training data. At the same time, disentanglement can improve the long-term and out-of-distribution predictions of state-of-the-art models in video sequences.

Related papers

• Data-driven Modeling of Parameterized Nonlinear Fluid Dynamical Systems with a Dynamics-embedded Conditional Generative Adversarial Network [0.0]
  We present a data-driven solution to accurately predict parameterized nonlinear fluid dynamical systems using a dynamics-generator conditional GAN (Dyn-cGAN) as a surrogate model. The learned Dyn-cGAN model takes into account the system parameters to predict the flow fields of the system accurately.
  arXiv  Detail & Related papers  (2024-12-23T20:50:20Z)
• Learning and Current Prediction of PMSM Drive via Differential Neural Networks [13.370017978792479]
  This study presents a novel approach utilizing differential neural networks (DNNs) to model nonlinear systems. The efficacy of our approach is validated through experiments conducted under various load disturbances and no-load conditions.
  arXiv  Detail & Related papers  (2024-12-12T07:43:27Z)
• Latent Space Energy-based Neural ODEs [73.01344439786524]
  This paper introduces novel deep dynamical models designed to represent continuous-time sequences. We train the model using maximum likelihood estimation with Markov chain Monte Carlo. Experimental results on oscillating systems, videos and real-world state sequences (MuJoCo) demonstrate that our model with the learnable energy-based prior outperforms existing counterparts.
  arXiv  Detail & Related papers  (2024-09-05T18:14:22Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• Temporal Domain Generalization with Drift-Aware Dynamic Neural Network [12.483886657900525]
  We propose a Temporal Domain Generalization with Drift-Aware Dynamic Neural Network (DRAIN) framework. Specifically, we formulate the problem into a Bayesian framework that jointly models the relation between data and model dynamics. It captures the temporal drift of model parameters and data distributions and can predict models in the future without the presence of future data.
  arXiv  Detail & Related papers  (2022-05-21T20:01:31Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods [0.0]
  We propose an extension where different sets of differential equation parameters are allowed for observation sub-periods. We derive prediction targets from individual dynamic models of resilience in the application. Our approach is seen to successfully identify individual-level parameters of dynamic models that allows us to stably select predictors.
  arXiv  Detail & Related papers  (2022-02-15T13:53:42Z)
• Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems [12.319812075685956]
  We combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specially, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model. In order to alleviate the issue of inconsistency between training and predicting, we (i) propose using a hybrid output as input at next time step, which brings training and predicting into alignment.
  arXiv  Detail & Related papers  (2021-06-03T23:41:11Z)
• Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization [60.73540999409032]
  We show that expressive autoregressive dynamics models generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We also show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer.
  arXiv  Detail & Related papers  (2021-04-28T16:48:44Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.