SINDy(Sparse Identification of Nonlinear Dynamics) Application

Feb 18, 2024

SINDy Application for Statistical Analysis and Visualization of Bioaerosol Concentration Data

Project Description

  • Motivation: To automatically calculate and visualize measured bioaerosol concentration data.
  • Goal: To discover governing equations from time-series data, particularly identifying the underlying dynamical system that generates the observed data.

Contribution

Programming

  • Methodology: Developed software using Python and the PyQt5 library.
  • Features: The application includes 10 key features, each implemented through specific Python functions:
    • Importing and Displaying csv data
    • Log Transformation
    • Standardization
    • Target, input data, and order number selection
    • Plotting polynomial functions
    • Inserting threshold parameters
    • Plotting threshold functions
    • Inserting lambda values
    • Generating the final data equation
  • Visualization: Implemented using the Matplotlib library

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Figure 1: SINDy application main page
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Figure 2: Loading and displaying csv data
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Figure 3: Analyze and visualize environment factor data
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Figure 4: Target, input data, and order number selection window
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Figure 5: Distribution of CFU
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Figure 6: Polynomial Function

Skills

  • Python: Data calculation, analysis, and visualization.
  • Matplotlib: Visualization
  • PyQt5: Python-based GUI development
Research Data Management
Hyungjun Doh
Hyungjun Doh
Master’s Student

My research interests are Human-AI interaction and its practical applications, with a specific focus on Extended Reality, Task Guidance Systems, and AI-infused interfaces.