mlsanityData to 7 Oct 2026

Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations

arXiv 2405.05987 ↗First posted 8 May 2024cs.LG cs.CEcited by 11

Not on the map. Why?

Abstract (arXiv)

This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) -- also known as Scientific Machine Learning -- with particular focus to those PEML strategies developed to tackle dynamical systems' challenges. The need to go beyond Machine Learning (ML) strategies is driven by: (i) limited volume of informative data, (ii) avoiding accurate-but-wrong predictions; (iii) dealing with uncertainties; (iv) providing Explainable and Interpretable inferences. A general definition of PEML is provided by considering four physics and domain knowledge biases, and three broad groups of PEML approaches are discussed: physics-guided, physics-encoded and physics-informed. The advantages and challenges in developing PEML strategies for guiding high-consequence decision making in engineering applications involving complex dynamical systems, are presented.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 11 of its 31 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

11 of 31 references.

References · 11

  1. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  2. 2019Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systemscited by 203
  3. 2020Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systemscited by 160
  4. 2021Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modelingcited by 59
  5. 2021Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approachcited by 36
  6. 2022Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computingcited by 24
  7. 2023A spectrum of physics-informed Gaussian processes for regression in engineeringcited by 13
  8. 2022Physics-informed machine learning for Structural Health Monitoringcited by 8
  9. 2022On generative models as the basis for digital twinscited by 4
  10. 2023Bayesian system identification for structures considering spatial and temporal correlationcited by 3
  11. 2023A switching Gaussian process latent force model for the identification of mechanical systems with a discontinuous nonlinearitycited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 4.

11 papers.

  1. 2025Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Modelscited by 5
  2. 2025Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencodercited by 2
  3. 2025A Generalizable Physics-Enhanced State Space Model for Long-Term Dynamics Forecasting in Complex Environmentscited by 2
  4. 2025Adaptive Sensor Steering Strategy Using Deep Reinforcement Learning for Dynamic Data Acquisition in Digital Twinscited by 1
  5. 2025Variational Rank Reduction Autoencoders for Generative Thermal Designcited by 1
  6. 2026Controlled oscillation modeling using port-Hamiltonian neural networkscited by 1
  7. 2026PiGGO: Physics-Guided Learnable Graph Kalman Filters for Virtual Sensing of Nonlinear Dynamic Structures under Uncertaintycited by 1
  8. 2025Digital twin for virtual sensing of ferry quays via a Gaussian Process Latent Force Modelcited by 0
  9. 2025Can Transformers overcome the lack of data in the simulation of history-dependent flows?cited by 0
  10. 2026In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphscited by 0
  11. 2026From Grey-Box to Green-Box: When can Physics-Informed Machine Learning Reduce Carbon Footprints in Structural Health Monitoring?cited by 0