mlsanityData to 7 Oct 2026

Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution

arXiv 2103.14629 ↗First posted 26 Mar 2021physics.comp-ph cs.LG cs.SYcited by 14

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Abstract (arXiv)

Most modeling approaches lie in either of the two categories: physics-based or data-driven. Recently, a third approach which is a combination of these deterministic and statistical models is emerging for scientific applications. To leverage these developments, our aim in this perspective paper is centered around exploring numerous principle concepts to address the challenges of (i) trustworthiness and generalizability in developing data-driven models to shed light on understanding the fundamental trade-offs in their accuracy and efficiency, and (ii) seamless integration of interface learning and multifidelity coupling approaches that transfer and represent information between different entities, particularly when different scales are governed by different physics, each operating on a different level of abstraction. Addressing these challenges could enable the revolution of digital twin technologies for scientific and engineering applications.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 48 of its 409 references that are in the map. First posted on arXiv 26 Mar 2021.

Builds on

48 of 409 references.

References · 48

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36 more
  1. 2019Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networkscited by 144
  2. 2017Linearly-Recurrent Autoencoder Networks for Learning Dynamicscited by 142
  3. 2017Sparse identification of nonlinear dynamics for model predictive control in the low-data limitcited by 139
  4. 2019Digital Twin: Values, Challenges and Enablerscited by 81
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  6. 2020A Probabilistic Graphical Model Foundation for Enabling Predictive Digital Twins at Scalecited by 58
  7. 2018Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Modelcited by 57
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 6; 2023: 1; 2024: 3; 2025: 1; 2026: 2.

14 papers.

Sort:

  1. 2022Physics guided neural networks for modelling of non-linear dynamicscited by 20
  2. 2021Deep neural network enabled corrective source term approach to hybrid analysis and modelingcited by 16
  3. 2023Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directionscited by 7
  4. 2022Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approachcited by 6
  5. 2022Artificial intelligence-driven digital twin of a modern house demonstrated in virtual realitycited by 4
  6. 2024Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order modelscited by 4
  7. 2022Sparse deep neural networks for modeling aluminum electrolysis dynamicscited by 3
  8. 2022Decentralized digital twins of complex dynamical systemscited by 2
  9. 2024Predictive Digital Twin for Condition Monitoring Using Thermal Imagingcited by 2
  10. 2025Hybrid Modeling, Sim-to-Real Reinforcement Learning, and Large Language Model Driven Control for Digital Twinscited by 1
  11. 2022Prospects of federated machine learning in fluid dynamicscited by 0
  12. 2024Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order modelscited by 0
2 more
  1. 2026Quantum SEDONet: Spectrally-Embedded Quantum Deep Operator Networks for Partial Differential Equationscited by 0
  2. 2026SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Datacited by 0