An Expert Survey on Models and Digital Twins

• URL: http://arxiv.org/abs/2506.17313v1
• Date: Wed, 18 Jun 2025 10:58:32 GMT
• Title: An Expert Survey on Models and Digital Twins
• Authors: Jonathan Reif, Daniel Dittler, Milapji Singh Gill, Tamás Farkas, Valentin Stegmaier, Felix Gehlhoff, Tobias Kleinert, Michael Weyrich,
• Abstract summary: This study conducts an expert survey across multiple application domains to identify and analyze the challenges in utilizing diverse DMs within DTs.<n>The results reveal missing standardized interfaces, high manual adaptation effort, and limited support for model reuse across lifecycle phases.
• Score: 0.602276990341246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Digital Twins (DTs) are becoming increasingly vital for future industrial applications, enhancing monitoring, control, and optimization of physical assets. This enhancement is made possible by integrating various Digital Models (DMs) within DTs, which must interoperate to represent different system aspects and fulfill diverse application purposes. However, industry perspectives on the challenges and research needs for integrating these models are rarely obtained. Thus, this study conducts an expert survey across multiple application domains to identify and analyze the challenges in utilizing diverse DMs within DTs. The results reveal missing standardized interfaces, high manual adaptation effort, and limited support for model reuse across lifecycle phases, highlighting future research needs in automated model composition and semantics-based interoperability.

Related papers

• Anomaly Detection and Generation with Diffusion Models: A Survey [51.61574868316922]
  Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing.<n>Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest.<n>This survey aims to guide researchers and practitioners in leveraging DMs for innovative AD solutions across diverse applications.
  arXiv  Detail & Related papers  (2025-06-11T03:29:18Z)
• UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines [64.84631333071728]
  We introduce bfUnistage, a unified Transformer-based framework fortemporal modeling.<n>Our work demonstrates that a task-specific vision-text can build a generalizable model fortemporal learning.<n>We also introduce a temporal module to incorporate temporal dynamics explicitly.
  arXiv  Detail & Related papers  (2025-03-26T17:33:23Z)
• Joint Modeling in Recommendations: A Survey [46.000357352884926]
  Joint modeling approaches are central to overcoming limitations by integrating diverse tasks, scenarios, modalities, and behaviors in the recommendation process.<n>We define the scope of joint modeling through four distinct dimensions: multi-task, multi-scenario, multi-modal, and multi-behavior modeling.<n>We highlight several promising avenues for future exploration in joint modeling for recommendations and provide a concise conclusion to our findings.
  arXiv  Detail & Related papers  (2025-02-28T16:14:00Z)
• HEMM: Holistic Evaluation of Multimodal Foundation Models [91.60364024897653]
  Multimodal foundation models can holistically process text alongside images, video, audio, and other sensory modalities. It is challenging to characterize and study progress in multimodal foundation models, given the range of possible modeling decisions, tasks, and domains.
  arXiv  Detail & Related papers  (2024-07-03T18:00:48Z)
• Learning Paradigms and Modelling Methodologies for Digital Twins in Process Industry [1.1060425537315088]
  Digital Twins (DTs) are virtual replicas of physical manufacturing systems that combine sensor data with sophisticated data-based or physics-based models, or a combination thereof, to tackle a variety of industrial-relevant tasks like process monitoring, predictive control or decision support. The backbone of a DT, i.e. the concrete modelling methodologies and architectural frameworks supporting these models, are complex, diverse and evolve fast, necessitating a thorough understanding of the latest state-of-the-art methods and trends to stay on top of a highly competitive market.
  arXiv  Detail & Related papers  (2024-07-02T14:05:10Z)
• MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild [81.32127423981426]
  Multimodal emotion recognition based on audio and video data is important for real-world applications. Recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multimodal encoders. We propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders.
  arXiv  Detail & Related papers  (2024-04-13T13:39:26Z)
• Demystifying Digital Twin Buzzword: A Novel Generic Evaluation Model [0.0]
  Despite the growing popularity of digital twins (DT) developments, there is a lack of common understanding and definition for important concepts of DT. This article proposes a four-dimensional evaluation framework to assess the maturity of digital twins across different domains.
  arXiv  Detail & Related papers  (2023-11-21T19:56:26Z)
• Current Trends in Digital Twin Development, Maintenance, and Operation: An Interview Study [0.2871849986181679]
  Digital twins (DT) are often defined as a pairing of a physical entity and a corresponding virtual entity (VE) We performed a semi-structured interview research with 19 professionals from industry and academia who are closely associated with different lifecycle stages of digital twins.
  arXiv  Detail & Related papers  (2023-06-16T12:19:28Z)
• Quantifying & Modeling Multimodal Interactions: An Information Decomposition Framework [89.8609061423685]
  We propose an information-theoretic approach to quantify the degree of redundancy, uniqueness, and synergy relating input modalities with an output task. To validate PID estimation, we conduct extensive experiments on both synthetic datasets where the PID is known and on large-scale multimodal benchmarks. We demonstrate their usefulness in (1) quantifying interactions within multimodal datasets, (2) quantifying interactions captured by multimodal models, (3) principled approaches for model selection, and (4) three real-world case studies.
  arXiv  Detail & Related papers  (2023-02-23T18:59:05Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)

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.