mlsanityData to 14 Oct 2026

Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications

arXiv 2310.20425 ↗First posted 31 Oct 2023cs.LGcited by 7

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Abstract (arXiv)

The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of physics-enhanced machine learning methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications and developments of PEML techniques, revealing the potency of PEML in addressing complex challenges. We further demonstrate application of select such schemes on the simple working example of a single degree-of-freedom Duffing oscillator, which allows to highlight the individual characteristics and motivations of different `genres' of PEML approaches. To promote collaboration and transparency, and to provide practical examples for the reader, the code generating these working examples is provided alongside this paper. As a foundational contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research, underpinned by the synergy of physical insights and machine learning capabilities.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 55 of its 166 references that are in the map. First posted on arXiv 31 Oct 2023.

Builds on

55 of 166 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networkscited by 4
  2. 2025Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamicscited by 4
  3. 2025Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencodercited by 2
  4. 2026PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertaintycited by 1
  5. 2025Digital twin for virtual sensing of ferry quays via a Gaussian Process Latent Force Modelcited by 0
  6. 2026From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?cited by 0
  7. 2026Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systemscited by 0