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Related Experiment Video

Updated: Dec 25, 2025

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales
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Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

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Learning partial differential equations for biological transport models from noisy spatio-temporal data.

John H Lagergren1,2, John T Nardini1,3, G Michael Lavigne1,2

  • 1Department of Mathematics, North Carolina State University, Raleigh, NC, USA.

Proceedings. Mathematical, Physical, and Engineering Sciences
|March 24, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial neural network (ANN) method for denoising spatio-temporal data to discover partial differential equations (PDEs). The ANN approach accurately approximates derivatives, outperforming traditional methods for biological transport models.

Keywords:
biological transportequation learningnumerical differentiationparameter estimationpartial differential equationssparse regression

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Area of Science:

  • Computational Biology
  • Applied Mathematics
  • Scientific Machine Learning

Background:

  • Learning partial differential equations (PDEs) from data is crucial for modeling complex systems.
  • Existing methods often struggle with biologically realistic noise levels in spatio-temporal data.
  • Denoising and accurate derivative approximation are key challenges in data-driven PDE discovery.

Purpose of the Study:

  • To evaluate existing denoising techniques for PDE discovery from noisy spatio-temporal data.
  • To develop and validate a novel artificial neural network (ANN) based methodology for denoising and derivative approximation.
  • To compare the performance of the ANN approach against traditional methods on biological transport models.

Main Methods:

  • Analysis of sparse regression techniques for PDE term selection after data denoising.
  • Development of an ANN-based framework for simultaneous data denoising and partial derivative approximation.
  • Testing the methodology on advection-diffusion, Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP), and nonlinear Fisher-KPP equations.

Main Results:

  • The ANN methodology demonstrated superior performance in approximating partial derivatives compared to finite differences and polynomial regression splines.
  • The proposed ANN approach effectively denoises spatio-temporal data under biologically relevant noise conditions.
  • Accurate derivative approximation by the ANN method facilitated more reliable discovery of the underlying PDE models.

Conclusions:

  • Artificial neural networks offer a powerful and accurate alternative for denoising and derivative estimation in data-driven PDE discovery.
  • The developed ANN methodology significantly enhances the ability to learn governing PDEs from noisy biological transport data.
  • This work advances the field of scientific machine learning for uncovering complex dynamical systems.