Numerical Identification of Nonlocal Potential in Aggregation
Abstract
Aggregation equations are broadly used to model population dynamics with nonlocal interactions, characterized by a potential in the equation. This paper considers the inverse problem of identifying the potential from a single noisy spatial-temporal process. The identification is challenging in the presence of noise due to the instability of numerical differentiation. We propose a robust model-based technique to identify the potential by minimizing a regularized data fidelity term, and regularization is taken as the total variation and the squared Laplacian. A split Bregman method is used to solve the regularized optimization problem. Our method is robust to noise by utilizing a Successively Denoised Differentiation technique. We consider additional constraints such as compact support and symmetry constraints to enhance the performance further. We also apply this method to identify time-varying potentials and identify the interaction kernel in an agent-based system. Various numerical examples in one and two dimensions are included to verify the effectiveness and robustness of the proposed method.
Cite
@article{arxiv.2207.03358,
title = {Numerical Identification of Nonlocal Potential in Aggregation},
author = {Yuchen He and Sung Ha Kang and Wenjing Liao and Hao Liu and Yingjie Liu},
journal= {arXiv preprint arXiv:2207.03358},
year = {2022}
}