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Related Experiment Video

Updated: Jul 5, 2025

Surrogate Model Development for Digital Experiments in Welding
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WEAK SINDy: GALERKIN-BASED DATA-DRIVEN MODEL SELECTION.

Daniel A Messenger1, David M Bortz1

  • 1Department of Applied Mathematics, University of Colorado, Boulder, CO 80309-0526 USA.

Multiscale Modeling & Simulation : a SIAM Interdisciplinary Journal
|January 19, 2024
PubMed
Summary
This summary is machine-generated.

We developed weak SINDy (WSINDy), a new method for discovering differential equations from noisy data. WSINDy reliably identifies models even with significant noise, improving accuracy and enabling robust predictions.

Keywords:
37M1062-0762J9965R99Galerkin methodadaptive griddata-driven model selectiongeneralized least squaresnonlinear dynamicssparse recovery

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Area of Science:

  • Scientific computing
  • Data-driven modeling
  • Differential equations

Background:

  • Discovering governing equations from data is crucial for scientific modeling.
  • Existing methods like SINDy struggle with noisy datasets.
  • Accurate equation discovery requires robust handling of measurement errors.

Purpose of the Study:

  • To introduce a novel weak formulation and discretization for learning differential equations from noisy data.
  • To develop a robust and accurate method for sparse recovery of governing equations.
  • To improve upon the standard SINDy algorithm for noisy data scenarios.

Main Methods:

  • Developed a weak formulation replacing pointwise derivative approximations with linear transformations and variance reduction.
  • Introduced the weak SINDy (WSINDy) algorithm.
  • Utilized integration for natural noise reduction, inspired by Schaeffer and McCalla (2017).

Main Results:

  • WSINDy enables reliable model identification from data with high noise levels (ratios > 0.1).
  • The algorithm reduces coefficient error, leading to accurate predictions.
  • Coefficient error scales linearly with noise, ensuring high accuracy in low-noise conditions.

Conclusions:

  • WSINDy offers a robust and accurate alternative to standard SINDy for noisy data.
  • The method combines SINDy's efficiency with noise reduction capabilities.
  • WSINDy facilitates reliable sparse recovery of differential equations from empirical measurements.