PINN-Based Solution for a Diffusion Controlled Droplet Growth

Fuente: arXiv
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Main Authors: Gol'din, Pavel, Gor, Gennady Y.
Format: Preprint
Published: 2026
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author Gol'din, Pavel
Gor, Gennady Y.
author_facet Gol'din, Pavel
Gor, Gennady Y.
contents We study diffusion-controlled growth of a spherical droplet with a moving boundary using a physics-informed neural network (PINN) formulation. The governing diffusion equation is coupled to the interfacial mass balance, with the droplet radius treated as an additional trainable function of time. The PINN accurately reproduces the self-similar growth law and concentration profiles for a wide range of initial droplet radii, demonstrating convergence toward the asymptotic diffusive regime. The proposed approach provides a flexible and computationally efficient framework for solving moving-boundary diffusion problems and can be readily extended to include additional physical effects.
format Preprint
id arxiv_https___arxiv_org_abs_2601_05042
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle PINN-Based Solution for a Diffusion Controlled Droplet Growth
Gol'din, Pavel
Gor, Gennady Y.
Fluid Dynamics
Computational Physics
We study diffusion-controlled growth of a spherical droplet with a moving boundary using a physics-informed neural network (PINN) formulation. The governing diffusion equation is coupled to the interfacial mass balance, with the droplet radius treated as an additional trainable function of time. The PINN accurately reproduces the self-similar growth law and concentration profiles for a wide range of initial droplet radii, demonstrating convergence toward the asymptotic diffusive regime. The proposed approach provides a flexible and computationally efficient framework for solving moving-boundary diffusion problems and can be readily extended to include additional physical effects.
title PINN-Based Solution for a Diffusion Controlled Droplet Growth
topic Fluid Dynamics
Computational Physics
url https://arxiv.org/abs/2601.05042