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Knowledge-guided Machine Learning: Current Trends and Future Prospects

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arxiv 2403.15989 v2 pith:6TXHULX2 submitted 2024-03-24 cs.LG cs.AIcs.CE

Anuj Karpatne , Xiaowei Jia , Vipin Kumar This is my paper · ORCID
classification cs.LGcs.AIcs.CE
keywords scientifickgmlknowledgecurrentdiscussknowledge-guidedlearningmachine
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PIER: Physics-Informed Environmental Retrieval for Time-Series Modeling

    cs.LG 2026-07 conditional novelty 5.0 of 10

    PIER augments embedding-based retrieval for lake modeling with a physics-aware stream scored by local verifiers, improving water temperature and dissolved oxygen prediction across 356 lakes.

  2. LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks

    cs.LG 2026-07 conditional novelty 5.0 of 10

    Meta-learning on easy PDE tasks plus a layer-wise gating schedule reduces extrapolation error by about 91% relative to six PINN baselines on hard convection, Helmholtz, and Navier-Stokes benchmarks.

  3. Physics-Encoded Inverse Modeling for Arctic Snow Depth Estimation

    cs.LG 2026-01 reject novelty 3.0 of 10

    PhysE-Inv predicts a snow-depth proxy generated from the same ERA5 inputs the model sees, so its accuracy against real Arctic snow depth is unestablished.

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