Control of the Fisher-Stefan System

Abstract This paper addresses the exact controllability of trajectories in the one-dimensional Fisher-Stefan problem—a reaction-diffusion equation that models the spatial propagation of biological, chemical, or physical populations within a free-end domain, governed by Stefan’s law. We establish the local exact controllability of the trajectories by reformulating the problem as the local null controllability of a nonlinear system with distributed controls. Our approach leverages the Lyusternik-Graves theorem to achieve local inversion, leading to the desired controllability result. Finally, we illustrate our theoretical findings through several numerical experiments based on the physics-informed neural networks (PINNs) approach.

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Publication Details

Journal
Communications on Applied Mathematics and Computation
Published
2026-05-29
DOI
https://doi.org/10.1007/s42967-026-00582-x
Primary Topic
Neural Networks Stability and Synchronization
Type
preprint
Field-Weighted Citation Impact
0.00

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preprint

Control of the Fisher-Stefan System

Francisco Periago, Idriss Boutaayamou, Lahcen Maniar, Fouad Et-tahri
Communications on Applied Mathematics and Computation
Neural Networks Stability and Synchronization
preprint

Control of the Fisher-Stefan System

Francisco Periago, Idriss Boutaayamou, Lahcen Maniar, Fouad Et-tahri
preprint en

Abstract

Abstract This paper addresses the exact controllability of trajectories in the one-dimensional Fisher-Stefan problem—a reaction-diffusion equation that models the spatial propagation of biological, chemical, or physical populations within a free-end domain, governed by Stefan’s law. We establish the local exact controllability of the trajectories by reformulating the problem as the local null controllability of a nonlinear system with distributed controls. Our approach leverages the Lyusternik-Graves theorem to achieve local inversion, leading to the desired controllability result. Finally, we illustrate our theoretical findings through several numerical experiments based on the physics-informed neural networks (PINNs) approach.

Communications on Applied Mathematics and Computation
Cadi Ayyad University (MA), Universidad Politécnica de Cartagena (ES), Université Ibn Zohr (MA), Office Régional de Mise en Valeur Agricole de Ouarzazate (MA)
Ministry of Science and Technology, Fundación Séneca, Universidad Politécnica de Cartagena
Openalex Percentile: Top 99%
Neural Networks Stability and Synchronization
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