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Inverse resolution of spatially varying diffusion coefficient using Physics-Informed neural networks
Sukirt Thakur1, Ehsan Esmaili1, Sarah Libring2
1School of Mechanical Engineering,Purdue University, West Lafayette, 47907, Indiana, USA.
Arxiv
|March 18, 2024
Summary
This study introduces a new method using physics-informed neural networks (PINNs) to accurately determine spatially-varying diffusion coefficients from image data. The approach shows high accuracy for both numerical and experimental datasets, aiding applications like drug delivery systems.
Area of Science:
- Multidisciplinary science
- Computational modeling
- Biophysics
Background:
- Accurate determination of diffusion coefficients is crucial for biological and engineering systems.
- Spatially varying diffusion coefficients present challenges in inverse problems with discrete data.
- Existing methods struggle with noisy and ill-posed datasets for diffusion coefficient determination.
Conclusions:
- Physics-informed neural networks (PINNs) offer a powerful tool for resolving spatially-varying diffusion coefficients.
- The developed method is robust and accurate for diverse biological and engineering applications.
- Potential applications include improved design of drug-eluting implants in regenerative medicine and oncology.
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