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Predictive Modeling of Flexible EHD Pumps using Kolmogorov-Arnold Networks

arXiv 2405.07488 ↗First posted 13 May 2024cs.LG cs.RO cs.SCcited by 23

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Abstract (arXiv)

We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov-Arnold Network. Inspired by the Kolmogorov-Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectively than traditional models like Multi-Layer Perceptron and Random Forest. We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.001 for pressure and flow rate predictions, respectively. The symbolic formulas extracted from KAN provided insights into the nonlinear relationships between input parameters and pump performance. These findings demonstrate that KAN offers exceptional accuracy and interpretability, making it a promising alternative for predictive modeling in electrohydrodynamic pumping.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 1 of its 7 references that are in the map. First posted on arXiv 13 May 2024.

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1 of 7 references.

References · 1

  1. 2024KAN: Kolmogorov-Arnold Networkscited by 634

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 12; 2025: 8; 2026: 3.

23 papers.

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  1. 2024KAN 2.0: Kolmogorov-Arnold Networks Meet Sciencecited by 116
  2. 2024A Survey on Kolmogorov-Arnold Networkcited by 67
  3. 2024KAGNNs: Kolmogorov-Arnold Networks meet Graph Learningcited by 48
  4. 2024On the expressiveness and spectral bias of KANscited by 36
  5. 2024rKAN: Rational Kolmogorov-Arnold Networkscited by 31
  6. 2024Reduced Effectiveness of Kolmogorov-Arnold Networks on Functions with Noisecited by 17
  7. 2024Endowing Interpretability for Neural Cognitive Diagnosis by Efficient Kolmogorov-Arnold Networkscited by 12
  8. 2024Generalization Bounds and Model Complexity for Kolmogorov-Arnold Networkscited by 11
  9. 2024Addressing common misinterpretations of KART and UAT in neural network literaturecited by 9
  10. 2024KAN versus MLP on Irregular or Noisy Functionscited by 7
  11. 2025MatrixKAN: Parallelized Kolmogorov-Arnold Networkcited by 7
  12. 2025Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Studycited by 7
11 more
  1. 2024KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics examplecited by 4
  2. 2024KAN v.s. MLP for Offline Reinforcement Learningcited by 3
  3. 2025Forecasting VIX using interpretable Kolmogorov-Arnold networkscited by 3
  4. 2025Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometriescited by 3
  5. 2026SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networkscited by 2
  6. 2025KAN-Mixers: a new deep learning architecture for image classificationcited by 1
  7. 2025PO-CKAN:Physics Informed Deep Operator Kolmogorov Arnold Networks with Chunk Rational Structurecited by 1
  8. 2026A Dynamic Framework for Grid Adaptation in Kolmogorov-Arnold Networkscited by 1
  9. 2025HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagationcited by 0
  10. 2025Traffic flow forecasting, STL decomposition, Hybrid model, LSTM, ARIMA, XGBoost, Intelligent transportation systemscited by 0
  11. 2026LLM-VLM Fusion Framework for Autonomous Maritime Port Inspection using a Heterogeneous UAV-USV Systemcited by 0