Quantifying and combining uncertainty for improving the behavior of Digital Twin Systems

• URL: http://arxiv.org/abs/2402.10535v1
• Date: Fri, 16 Feb 2024 09:46:40 GMT
• Title: Quantifying and combining uncertainty for improving the behavior of Digital Twin Systems
• Authors: Julien Deantoni and Paula Mu\~noz and Cl\'audio Gomes and Clark Verbrugge and Rakshit Mittal and Robert Heinrich and Stijn Bellis and Antonio Vallecillo
• Abstract summary: We focus on the Digital Twins of adaptive systems, which are particularly complex to design, verify, and optimize. We propose the explicit representation and treatment of the uncertainty of both twins, and show how this enables a more accurate comparison of their behaviors.
• Score: 1.7726648147818043
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty is an inherent property of any complex system, especially those that integrate physical parts or operate in real environments. In this paper, we focus on the Digital Twins of adaptive systems, which are particularly complex to design, verify, and optimize. One of the problems of having two systems (the physical one and its digital replica) is that their behavior may not always be consistent. In addition, both twins are normally subject to different types of uncertainties, which complicates their comparison. In this paper we propose the explicit representation and treatment of the uncertainty of both twins, and show how this enables a more accurate comparison of their behaviors. Furthermore, this allows us to reduce the overall system uncertainty and improve its behavior by properly averaging the individual uncertainties of the two twins. An exemplary incubator system is used to illustrate and validate our proposal.

Related papers

• 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)
• Clarifying System 1 & 2 through the Common Model of Cognition [0.0]
  We use the Common Model of Cognition to ground System-1 and System-2. We aim to clarify their underlying mechanisms, persisting misconceptions, and implications for metacognition.
  arXiv  Detail & Related papers  (2023-05-18T02:25:03Z)
• Stable Bias: Analyzing Societal Representations in Diffusion Models [72.27121528451528]
  We propose a new method for exploring the social biases in Text-to-Image (TTI) systems. Our approach relies on characterizing the variation in generated images triggered by enumerating gender and ethnicity markers in the prompts. We leverage this method to analyze images generated by 3 popular TTI systems and find that while all of their outputs show correlations with US labor demographics, they also consistently under-represent marginalized identities to different extents.
  arXiv  Detail & Related papers  (2023-03-20T19:32:49Z)
• Causal Falsification of Digital Twins [33.567972948107005]
  Digital twins are virtual systems designed to predict how a real-world process will evolve in response to interventions. We consider how to assess the accuracy of a digital twin using real-world data.
  arXiv  Detail & Related papers  (2023-01-17T22:18:53Z)
• 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)
• Degradation-agnostic Correspondence from Resolution-asymmetric Stereo [96.03964515969652]
  We study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. We propose to impose the consistency between two views in a feature space instead of the image space, named feature-metric consistency. We find that, although a stereo matching network trained with the photometric loss is not optimal, its feature extractor can produce degradation-agnostic and matching-specific features.
  arXiv  Detail & Related papers  (2022-04-04T12:24:34Z)
• Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning [49.6928533575956]
  We use neural inference to mediate between the neural System 1 and the logical System 2. Results in robust story generation and grounded instruction-following show that this approach can increase the coherence and accuracy of neurally-based generations.
  arXiv  Detail & Related papers  (2021-07-06T17:59:49Z)
• Hidden time-reversal symmetry, quantum detailed balance and exact solutions of driven-dissipative quantum systems [0.0]
  Driven-dissipative quantum systems generically do not satisfy simple notions of detailed balance based on the time symmetry of correlation functions. We show that such systems can nonetheless exhibit a hidden time-reversal symmetry which most directly manifests itself in a doubled version of the original system. This hidden time-reversal symmetry has a direct operational utility: it provides a general method for finding exact solutions of non-trivial steady states.
  arXiv  Detail & Related papers  (2020-11-04T06:21:44Z)
• Digital Twins: State of the Art Theory and Practice, Challenges, and Open Research Questions [62.67593386796497]
  This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. The major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics.
  arXiv  Detail & Related papers  (2020-11-02T19:08:49Z)
• Distributing entanglement with separable states: assessment of encoding and decoding imperfections [55.41644538483948]
  Entanglement can be distributed using a carrier which is always separable from the rest of the systems involved. We consider the effect of incoherent dynamics acting alongside imperfect unitary interactions. We show that entanglement gain is possible even with substantial unitary errors.
  arXiv  Detail & Related papers  (2020-02-11T15:25:19Z)
• 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.