mlsanityData to 7 Oct 2026

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning

arXiv 2109.13901 ↗First posted 28 Sep 2021cs.LG physics.class-ph physics.comp-phcited by 11

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Abstract (arXiv)

Integrating physical inductive biases into machine learning can improve model generalizability. We generalize the successful paradigm of physics-informed learning (PIL) into a more general framework that also includes what we term physics-augmented learning (PAL). PIL and PAL complement each other by handling discriminative and generative properties, respectively. In numerical experiments, we show that PAL performs well on examples where PIL is inapplicable or inefficient.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 15 of its 21 references that are in the map. First posted on arXiv 28 Sep 2021.

Builds on

15 of 21 references.

References · 15

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  2. 2016Group Equivariant Convolutional Networkscited by 1,154
  3. 2021E(n) Equivariant Graph Neural Networkscited by 951
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3 more
  1. 2019Learning Neural PDE Solvers with Convergence Guaranteescited by 77
  2. 2021Machine-learning hidden symmetriescited by 31
  3. 2021Machine-Learning Non-Conservative Dynamics for New-Physics Detectioncited by 10

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Cited by year

Citing papers per year: 2021: 1; 2022: 7; 2023: 0; 2024: 2; 2025: 1.

11 papers.

  1. 2021Residual Pathway Priors for Soft Equivariance Constraintscited by 74
  2. 2022Deconstructing the Inductive Biases of Hamiltonian Neural Networkscited by 31
  3. 2022Partial Differential Equations Meet Deep Neural Networks: A Surveycited by 25
  4. 2022Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systemscited by 20
  5. 2022Competitive Physics Informed Networkscited by 19
  6. 2022Disentangled Spatiotemporal Graph Generative Modelscited by 12
  7. 2022Data Science and Machine Learning in Educationcited by 3
  8. 2024Decoding complexity: how machine learning is redefining scientific discoverycited by 3
  9. 2025Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energiescited by 3
  10. 2024Can Kans (re)discover predictive models for Direct-Drive Laser Fusion?cited by 1
  11. 2022Applications of AI in Astronomycited by 0