mlsanityData to 14 Oct 2026

Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems

arXiv 1903.12394 ↗First posted 29 Mar 2019stat.ML cs.AI cs.LGcited by 205

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Abstract (arXiv)

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning.

Key facts. Cited by 205 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Mar 2019.

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Citing papers per year: 2021: 32; 2022: 37; 2023: 38; 2024: 47; 2025: 26; 2026: 25.

205 papers.

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  1. 2021Characterizing possible failure modes in physics-informed neural networkscited by 643
  2. 2022A Survey of Knowledge Enhanced Pre-trained Language Modelscited by 76
  3. 2022When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learningcited by 65
  4. 2021Learning in Sinusoidal Spaces with Physics-Informed Neural Networkscited by 62
  5. 2021Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modelingcited by 59
  6. 2021Incorporating Domain Knowledge into Deep Neural Networkscited by 56
  7. 2021Explanation-Based Human Debugging of NLP Models: A Surveycited by 53
  8. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 53
  9. 2022Deep Learning with Logical Constraintscited by 44
  10. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  11. 2022Machine Learning in Aerodynamic Shape Optimizationcited by 39
  12. 2022A Survey on Explainable Anomaly Detectioncited by 39
38 more
  1. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 38
  2. 2022Compute-Efficient Deep Learning: Algorithmic Trends and Opportunitiescited by 38
  3. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 35
  4. 2021Constructing Neural Network-Based Models for Simulating Dynamical Systemscited by 31
  5. 2021Latent Space Data Assimilation by using Deep Learningcited by 30
  6. 2023TABLET: Learning From Instructions For Tabular Datacited by 28
  7. 2022Adaptive Self-supervision Algorithms for Physics-informed Neural Networkscited by 24
  8. 2022PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solverscited by 22
  9. 2021Injecting Knowledge in Data-driven Vehicle Trajectory Predictorscited by 18
  10. 2022Recognizing Object by Components with Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networkscited by 18
  11. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 17
  12. 2021Explainable Machine Learning with Prior Knowledge: An Overviewcited by 15
  13. 2023Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regressioncited by 14
  14. 2023A spectrum of physics-informed Gaussian processes for regression in engineeringcited by 14
  15. 2024Knowledge-guided Machine Learning: Current Trends and Future Prospectscited by 14
  16. 2022Finite Element Method-enhanced Neural Network for Forward and Inverse Problemscited by 13
  17. 2024Geometry-Informed Neural Networkscited by 12
  18. 2024Physics-Enhanced Machine Learning: a position paper for dynamical systems investigationscited by 12
  19. 2024Generalizing 6-DoF Grasp Detection via Domain Prior Knowledgecited by 11
  20. 2025Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunitiescited by 11
  21. 2022Knowledge-augmented Deep Learning and Its Applications: A Surveycited by 10
  22. 2023GLUECons: A Generic Benchmark for Learning Under Constraintscited by 9
  23. 2024ULLER: A Unified Language for Learning and Reasoningcited by 9
  24. 2021AI Descartes: Combining Data and Theory for Derivable Scientific Discoverycited by 8
  25. 2021A coarse space acceleration of deep-DDMcited by 8
  26. 2024Med-Real2Sim: Non-Invasive Medical Digital Twins using Physics-Informed Self-Supervised Learningcited by 8
  27. 2024Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learnerscited by 8
  28. 2022Requirements Engineering for Machine Learning: A Review and Reflectioncited by 7
  29. 2022Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Modelscited by 7
  30. 2023Unleashing the Power of Shared Label Structures for Human Activity Recognitioncited by 7
  31. 2024KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatchcited by 7
  32. 2024ATLAS: Adapter-Based Multi-Modal Continual Learning with a Two-Stage Learning Strategycited by 7
  33. 2025Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Reviewcited by 7
  34. 2022Causal Discovery and Knowledge Injection for Contestable Neural Networks (with Appendices)cited by 6
  35. 2023Navigating Alignment for Non-identical Client Class Sets: A Label Name-Anchored Federated Learning Frameworkcited by 6
  36. 2023From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Drivingcited by 6
  37. 2023Physics Inspired Hybrid Attention for SAR Target Recognitioncited by 6
  38. 2021Multicriteria interpretability driven Deep Learningcited by 5

50 most cited of 205 All 205 (JSON)