Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
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Tripura et al. [142] proposed an interpretable ML approach for updating DTs.
“Tripura et al. [142] proposed an interpretable ML approach for updating DTs.”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [142]
Their method utilizes interpretable physical and mathematical functions to model the dynamics of real world systems.
“Their method utilizes interpretable physical and mathematical functions to model the dynamics of real world systems.”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [142]
By leveraging sparse Bayesian regression, the approach accurately identifies critical components representing perturbation terms in the physical twins' underlying dynamics, ensuring precise updates to digital twins.
“By leveraging sparse Bayesian regression, the approach accurately identifies critical components representing perturbation terms in the physical twins' underlying dynamics, ensuring precise updates to digital twins.”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [142]
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- Authors
- Tripura, T., Desai, A. S., Adhikari, S., & Chakraborty, S.
- Affiliations
- Indian Institute of Technology Delhi (IN)
- University of Glasgow (GB)
- Corresponding author
- Tripura, Tapas
- Chakraborty, Souvik
- Venue
- Computers & Structures
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 281
- Pages
- 107008
- ISSN
- 0045-7949 (ISSN-L)
- OpenAlex
- W4323662809
- Cited by
- 47
- References
- 51
- Citations by year
- 2026: 14 · 2025: 15 · 2024: 12 · 2023: 6
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Computers & Structures (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Ministry of Education, India
- Indian Institute of Technology Delhi
- Science and Engineering Research Board (SRG/2021/000467)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- digital twin, sparse Bayesian learning, dynamical systems, physics-informed surrogate models, output-only modal identification, epistemic uncertainty quantification, Itô calculus, Kramers-Moyal expansion, model updating, degradation monitoring, cyber-physical integration
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