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October 31, 2025East Asian Journal on Applied Mathematics

Improved Random Feature Method for Continuous Solution Reconstruction from Sparse Observation

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Authors

QLQingchun LiQingdao UniversityQXQinwu XuHankou University

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Implication

Improved random feature method reduces interpolation errors in solution reconstruction, suggesting benefits for complex domains.

Key Points

  • Improved random feature method enhances solution reconstruction using observational data and models, boosting accuracy.
  • Key comparisons indicate enhanced convergence and accuracy over traditional methods, validated against neural networks.
  • The method employs global activation functions and optimal cost function criteria to streamline interpolation processes.
  • Findings may enable improved simulations and forecasts in computational models dealing with sparse data.

Cite This Study

Li et al. (2025) studied this question.

synapsesocial.com/papers/6903feedb25c631a426600d0https://doi.org/10.4208/eajam.2024-210.060425
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