Digital twins of nonlinear dynamical systems
- URL: http://arxiv.org/abs/2210.06144v1
- Date: Wed, 5 Oct 2022 23:57:05 GMT
- Title: Digital twins of nonlinear dynamical systems
- Authors: Ling-Wei Kong, Yang Weng, Bryan Glaz, Mulugeta Haile, and Ying-Cheng
Lai
- Abstract summary: We show that the digital twins can extrapolate the dynamics of the target system to certain parameter regimes never experienced before.
We make our digital twins appealing in significant applications such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes.
- Score: 2.577161800553927
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We articulate the design imperatives for machine-learning based digital twins
for nonlinear dynamical systems subject to external driving, which can be used
to monitor the ``health'' of the target system and anticipate its future
collapse. We demonstrate that, with single or parallel reservoir computing
configurations, the digital twins are capable of challenging forecasting and
monitoring tasks. Employing prototypical systems from climate, optics and
ecology, we show that the digital twins can extrapolate the dynamics of the
target system to certain parameter regimes never experienced before, make
continual forecasting/monitoring with sparse real-time updates under
non-stationary external driving, infer hidden variables and accurately predict
their dynamical evolution, adapt to different forms of external driving, and
extrapolate the global bifurcation behaviors to systems of some different
sizes. These features make our digital twins appealing in significant
applications such as monitoring the health of critical systems and forecasting
their potential collapse induced by environmental changes.
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) - Digital twins of nonlinear dynamical systems: A perspective [0.0]
Digital twins of nonlinear dynamical systems can generate the system evolution and predict potentially catastrophic emergent behaviors.
The digital twin can then be used for system "health" monitoring in real time and for predictive problem solving.
Two approaches exist for constructing digital twins of nonlinear dynamical systems: sparse optimization and machine learning.
arXiv Detail & Related papers (2023-09-20T16:57:11Z) - A digital twin framework for civil engineering structures [0.6249768559720122]
The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms.
This work proposes a predictive digital twin approach to the health monitoring, maintenance, and management planning of civil engineering structures.
arXiv Detail & Related papers (2023-08-02T21:38:36Z) - Probabilistic machine learning based predictive and interpretable
digital twin for dynamical systems [0.0]
Two approaches for updating the digital twin are proposed.
In both cases, the resulting expressions of updated digital twins are identical.
The proposed approaches provide an exact and explainable description of the perturbations in digital twin models.
arXiv Detail & Related papers (2022-12-19T04:25:59Z) - 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) - Supervised DKRC with Images for Offline System Identification [77.34726150561087]
Modern dynamical systems are becoming increasingly non-linear and complex.
There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control.
Our approach learns these basis functions using a supervised learning approach.
arXiv Detail & Related papers (2021-09-06T04:39:06Z) - 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) - An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear
Analysis [68.8204255655161]
We show that uncertainty measures based on Monte-Carlo dropout in the context of a human-in-the-loop system increase the system's transparency and performance.
A simulation study demonstrates that the uncertainty-based human-in-the-loop system increases performance for different levels of human involvement.
arXiv Detail & Related papers (2020-07-14T15:47:37Z) - Machine learning based digital twin for dynamical systems with multiple
time-scales [0.0]
Digital twin technology has a huge potential for widespread applications in different industrial sectors such as infrastructure, aerospace, and automotive.
Here we focus on a digital twin framework for linear single-degree-of-freedom structural dynamic systems evolving in two different operational time scales.
arXiv Detail & Related papers (2020-05-12T15:33:25Z) - End-to-End Models for the Analysis of System 1 and System 2 Interactions
based on Eye-Tracking Data [99.00520068425759]
We propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events.
A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios.
We show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy.
arXiv Detail & Related papers (2020-02-03T17:46:13Z)
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.