SpReME: Sparse Regression for Multi-Environment Dynamic Systems
- URL: http://arxiv.org/abs/2302.05942v1
- Date: Sun, 12 Feb 2023 15:45:50 GMT
- Title: SpReME: Sparse Regression for Multi-Environment Dynamic Systems
- Authors: MoonJeong Park, Youngbin Choi, Namhoon Lee and Dongwoo Kim
- Abstract summary: We develop a method of sparse regression dubbed SpReME to discover the major dynamics that underlie multiple environments.
We demonstrate that the proposed model captures the correct dynamics from multiple environments over four different dynamic systems with improved prediction performance.
- Score: 6.7053978622785415
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Learning dynamical systems is a promising avenue for scientific discoveries.
However, capturing the governing dynamics in multiple environments still
remains a challenge: model-based approaches rely on the fidelity of assumptions
made for a single environment, whereas data-driven approaches based on neural
networks are often fragile on extrapolating into the future. In this work, we
develop a method of sparse regression dubbed SpReME to discover the major
dynamics that underlie multiple environments. Specifically, SpReME shares a
sparse structure of ordinary differential equation (ODE) across different
environments in common while allowing each environment to keep the coefficients
of ODE terms independently. We demonstrate that the proposed model captures the
correct dynamics from multiple environments over four different dynamic systems
with improved prediction performance.
Related papers
- Learning System Dynamics without Forgetting [60.08612207170659]
Predicting trajectories of systems with unknown dynamics is crucial in various research fields, including physics and biology.
We present a novel framework of Mode-switching Graph ODE (MS-GODE), which can continually learn varying dynamics.
We construct a novel benchmark of biological dynamic systems, featuring diverse systems with disparate dynamics.
arXiv Detail & Related papers (2024-06-30T14:55:18Z) - Generalizing Graph ODE for Learning Complex System Dynamics across
Environments [33.63818978256567]
GG-ODE is a machine learning framework for learning continuous multi-agent system dynamics across environments.
Our model learns system dynamics using neural ordinary differential equations (ODE) parameterized by Graph Neural Networks (GNNs)
Experiments over various physical simulations show that our model can accurately predict system dynamics, especially in the long range.
arXiv Detail & Related papers (2023-07-10T00:29:25Z) - 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) - Generalizing to New Physical Systems via Context-Informed Dynamics Model [0.0]
We propose a new framework for context-informed dynamics adaptation (CoDA)
CoDA learns to condition the dynamics model on contextual parameters, specific to each environment.
We show state-ofthe-art generalization results on a set of nonlinear dynamics, representative of a variety of application domains.
arXiv Detail & Related papers (2022-02-01T07:41:10Z) - LEADS: Learning Dynamical Systems that Generalize Across Environments [12.024388048406587]
We propose LEADS, a novel framework that leverages the commonalities and discrepancies among known environments to improve model generalization.
We show that this new setting can exploit knowledge extracted from environment-dependent data and improves generalization for both known and novel environments.
arXiv Detail & Related papers (2021-06-08T17:28:19Z) - Learning to Continuously Optimize Wireless Resource in a Dynamic
Environment: A Bilevel Optimization Perspective [52.497514255040514]
This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment.
We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes.
Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
arXiv Detail & Related papers (2021-05-03T07:23:39Z) - Neural Ordinary Differential Equations for Data-Driven Reduced Order
Modeling of Environmental Hydrodynamics [4.547988283172179]
We explore the use of Neural Ordinary Differential Equations for fluid flow simulation.
Test problems we consider include incompressible flow around a cylinder and real-world applications of shallow water hydrodynamics in riverine and estuarine systems.
Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions.
arXiv Detail & Related papers (2021-04-22T19:20:47Z) - Learning to Continuously Optimize Wireless Resource In Episodically
Dynamic Environment [55.91291559442884]
This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment.
We propose to build the notion of continual learning into the modeling process of learning wireless systems.
Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
arXiv Detail & Related papers (2020-11-16T08:24:34Z) - Trajectory-wise Multiple Choice Learning for Dynamics Generalization in
Reinforcement Learning [137.39196753245105]
We present a new model-based reinforcement learning algorithm that learns a multi-headed dynamics model for dynamics generalization.
We incorporate context learning, which encodes dynamics-specific information from past experiences into the context latent vector.
Our method exhibits superior zero-shot generalization performance across a variety of control tasks, compared to state-of-the-art RL methods.
arXiv Detail & Related papers (2020-10-26T03:20:42Z) - Variational Dynamic Mixtures [18.730501689781214]
We develop variational dynamic mixtures (VDM) to infer sequential latent variables.
In an empirical study, we show that VDM outperforms competing approaches on highly multi-modal datasets.
arXiv Detail & Related papers (2020-10-20T16:10:07Z) - Context-aware Dynamics Model for Generalization in Model-Based
Reinforcement Learning [124.9856253431878]
We decompose the task of learning a global dynamics model into two stages: (a) learning a context latent vector that captures the local dynamics, then (b) predicting the next state conditioned on it.
In order to encode dynamics-specific information into the context latent vector, we introduce a novel loss function that encourages the context latent vector to be useful for predicting both forward and backward dynamics.
The proposed method achieves superior generalization ability across various simulated robotics and control tasks, compared to existing RL schemes.
arXiv Detail & Related papers (2020-05-14T08:10:54Z)
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.