System Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters

• URL: http://arxiv.org/abs/2506.10434v1
• Date: Thu, 12 Jun 2025 07:35:30 GMT
• Title: System Identification Using Kolmogorov-Arnold Networks: A Case Study on Buck Converters
• Authors: Nart Gashi, Panagiotis Kakosimos, George Papafotiou,
• Abstract summary: Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification.<n>This paper investigates the application of KANs to model and analyze the dynamics of a buck converter system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Kolmogorov-Arnold Networks (KANs) are emerging as a powerful framework for interpretable and efficient system identification in dynamic systems. By leveraging the Kolmogorov-Arnold representation theorem, KANs enable function approximation through learnable activation functions, offering improved scalability, accuracy, and interpretability compared to traditional neural networks. This paper investigates the application of KANs to model and analyze the dynamics of a buck converter system, focusing on state-space parameter estimation along with discovering the system equations. Using simulation data, the methodology involves approximating state derivatives with KANs, constructing interpretable state-space representations, and validating these models through numerical experiments. The results demonstrate the ability of KANs to accurately identify system dynamics, verify model consistency, and detect parameter changes, providing valuable insights into their applicability for system identification in modern industrial systems.

Related papers

• Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics [3.9000699798128338]
  We propose a novel framework that integrates structured state-space modeling with the Kolmogorov-Arnold Network (KAN)<n>SKANODE first employs a fully trainable KAN as a universal function approximator within a structured Neural ODE framework to perform virtual sensing.<n>We exploit the symbolic regression capability of KAN to extract compact and interpretable expressions for the system's governing dynamics.
  arXiv  Detail & Related papers  (2025-06-23T06:42:43Z)
• Certified Neural Approximations of Nonlinear Dynamics [52.79163248326912]
  In safety-critical contexts, the use of neural approximations requires formal bounds on their closeness to the underlying system.<n>We propose a novel, adaptive, and parallelizable verification method based on certified first-order models.
  arXiv  Detail & Related papers  (2025-05-21T13:22:20Z)
• SINDyG: Sparse Identification of Nonlinear Dynamical Systems from Graph-Structured Data, with Applications to Stuart-Landau Oscillator Networks [0.27624021966289597]
  We develop a new method called Sparse Identification of Dynamical Systems from Graph-structured data (SINDyG)<n>SINDyG incorporates the network structure into sparse regression to identify model parameters that explain the underlying network dynamics.<n>Our experiments validate the improved accuracy and simplicity of discovered network dynamics.
  arXiv  Detail & Related papers  (2024-09-02T17:51:37Z)
• KAN-ODEs: Kolmogorov-Arnold Network Ordinary Differential Equations for Learning Dynamical Systems and Hidden Physics [0.0]
  Kolmogorov-Arnold networks (KANs) are an alternative to multi-layer perceptrons (MLPs) This work applies KANs as the backbone of a neural ordinary differential equation (ODE) framework.
  arXiv  Detail & Related papers  (2024-07-05T00:38:49Z)
• A Transition System Abstraction Framework for Neural Network Dynamical System Models [2.414910571475855]
  This paper proposes a transition system abstraction framework for neural network dynamical system models. The framework is able to abstract a data-driven neural network model into a transition system, making the neural network model interpretable.
  arXiv  Detail & Related papers  (2024-02-18T23:49:18Z)
• InVAErt networks: a data-driven framework for model synthesis and identifiability analysis [0.0]
  inVAErt is a framework for data-driven analysis and synthesis of physical systems. It uses a deterministic decoder to represent the forward and inverse maps, a normalizing flow to capture the probabilistic distribution of system outputs, and a variational encoder to learn a compact latent representation for the lack of bijectivity between inputs and outputs.
  arXiv  Detail & Related papers  (2023-07-24T07:58:18Z)
• ConCerNet: A Contrastive Learning Based Framework for Automated Conservation Law Discovery and Trustworthy Dynamical System Prediction [82.81767856234956]
  This paper proposes a new learning framework named ConCerNet to improve the trustworthiness of the DNN based dynamics modeling. We show that our method consistently outperforms the baseline neural networks in both coordinate error and conservation metrics.
  arXiv  Detail & Related papers  (2023-02-11T21:07:30Z)
• Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations [68.62843292346813]
  We propose a structured latent ODE model that captures system input variations within its latent representation. Building on a static variable specification, our model learns factors of variation for each input to the system, thus separating the effects of the system inputs in the latent space.
  arXiv  Detail & Related papers  (2022-02-25T20:00:56Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models [13.637931956861758]
  We show how we can approximate missing ordinary differential equations with known ODEs using a neural network approximation. Results indicate that this RBSE approach to training the NN parameters yields better outcomes (measurement/state estimation accuracy) than training the neural network with backpropagation.
  arXiv  Detail & Related papers  (2021-10-12T15:38:13Z)
• Supervised DKRC with Images for Offline System Identification [77.34726150561087]
  Modern dynamical systems are becoming increasingly non-linear and complex. There is a need for a framework to model these systems in a compact and comprehensive representation for prediction and control. Our approach learns these basis functions using a supervised learning approach.
  arXiv  Detail & Related papers  (2021-09-06T04:39:06Z)
• Discovering Sparse Interpretable Dynamics from Partial Observations [0.0]
  We propose a machine learning framework for discovering these governing equations using only partial observations. Our tests show that this method can successfully reconstruct the full system state.
  arXiv  Detail & Related papers  (2021-07-22T18:23:23Z)
• Meta-learning using privileged information for dynamics [66.32254395574994]
  We extend the Neural ODE Process model to use additional information within the Learning Using Privileged Information setting. We validate our extension with experiments showing improved accuracy and calibration on simulated dynamics tasks.
  arXiv  Detail & Related papers  (2021-04-29T12:18:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.