mlsanityData to 7 Oct 2026

Knowledge-guided Machine Learning: Current Trends and Future Prospects

arXiv 2403.15989 ↗First posted 24 Mar 2024cs.LG cs.AI cs.CEcited by 13

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Abstract (arXiv)

This paper presents an overview of scientific modeling and discusses the complementary strengths and weaknesses of ML methods for scientific modeling in comparison to process-based models. It also provides an introduction to the current state of research in the emerging field of scientific knowledge-guided machine learning (KGML) that aims to use both scientific knowledge and data in ML frameworks to achieve better generalizability, scientific consistency, and explainability of results. We discuss different facets of KGML research in terms of the type of scientific knowledge used, the form of knowledge-ML integration explored, and the method for incorporating scientific knowledge in ML. We also discuss some of the common categories of use cases in environmental sciences where KGML methods are being developed, using illustrative examples in each category.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 74 of its 179 references that are in the map. First posted on arXiv 24 Mar 2024.

Builds on

74 of 179 references.

References · 74

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  1. 2021Physics-Informed Neural Operator for Learning Partial Differential Equationscited by 516
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Citing papers per year: 2024: 5; 2025: 4; 2026: 4.

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  1. 2024Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systemscited by 25
  2. 2024Adaptive Process-Guided Learning: An Application in Predicting Lake DO Concentrationscited by 9
  3. 2024Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolutioncited by 7
  4. 2025A Language Anchor-Guided Method for Robust Noisy Domain Generalizationcited by 5
  5. 2025A Survey of Foundation Models for Environmental Sciencecited by 3
  6. 2025Knowledge Guided Encoder-Decoder Framework: Integrating Multiple Physical Models for Agricultural Ecosystem Modelingcited by 1
  7. 2024Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems: An application on Hydrological Systemscited by 0
  8. 2024What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traitscited by 0
  9. 2025Knowledge-Guided Machine Learning: Illustrating the use of Explainable Boosting Machines to Identify Overshooting Tops in Satellite Imagerycited by 0
  10. 2026Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimationcited by 0
  11. 2026CHAM-net: A Contrastive Hierarchical Adaptive Meta-network for Robust Global Methane Flux Predictioncited by 0
  12. 2026Knowledge-guided Pattern Discovery via Coupled Tensor Factorizationscited by 0
1 more
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